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1220 1221 1222 1223 1224 | """
Curriculum-aware math environment with dual reward signals.
This file is deliberately minimal: a single ``collect_rollouts`` method is all
the training loop needs. Rollouts and PPO updates run in the same process on
a single GPU — no subprocesses, no RPC, no vLLM colocation.
"""
from __future__ import annotations
import logging
import random
import re
from dataclasses import asdict, dataclass
from typing import Any, Dict, List, Optional, Tuple
import torch
from sympy import simplify
from sympy.parsing.sympy_parser import parse_expr
from tqdm.auto import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
from src.config.prompts import create_generator_messages, create_solver_messages
from src.rl.curriculum_manager import CurriculumManager
from src.rl.expert_panel import SimulatedExpertPanel
from src.rl.mdp_components import Action, State, Trajectory, Transition
from src.rl.prm_scorer import ProcessRewardScorer
from src.rl.quality_filter import QualityFilter
from src.rl.question_quality_evaluator import QuestionQualityEvaluator
from src.rl.replay_buffer import GenerationalReplayBuffer
from src.rl.value_network import ValueHead
from src.sft.solution_format import extract_final_answer_numeric_str
from src.sft.sympy_normalize import normalize_for_parse_expr
logger = logging.getLogger(__name__)
@dataclass
class TrajectoryMetadata:
curriculum_iteration: int
target_topic: str
target_difficulty: float
instruction: str
generated_question: str
generated_solution: str
question_length: int
solution_length: int
detected_topic: str
detected_secondary_topics: List[str]
topic_match_score: float
estimated_difficulty: float
clarity_score: float
novelty_scores: Dict[str, float]
consensus_achieved: bool
consensus_strength: float
answer_diversity: int
majority_answer: Optional[float]
primary_matches_majority: bool
sympy_verified: bool
steps_total: int
steps_verified_ok: int
steps_failed: int
final_answer_ok: bool
question_reward: float
solution_reward: float
pre_expert_reward: float
expert_reward_modifier: float
expert_phase: str
expert_feedback: str
replay_candidate: bool
replay_novelty: float
replay_added: bool
combined_reward: float
reward_breakdown: Dict[str, object]
topics_in_sweet_spot: List[str]
current_focus_topics: List[str]
curriculum_state_snapshot: Dict[str, object]
class CurriculumMathEnvironment:
"""Standalone curriculum environment with PRM-based rewards and GRPO training support."""
def __init__(
self,
policy_model: AutoModelForCausalLM,
value_model: Optional[ValueHead],
tokenizer: AutoTokenizer,
reference_questions: Optional[List[str]] = None,
grounded_qa_pairs: Optional[List[Dict[str, str]]] = None,
prm_scorer: Optional[ProcessRewardScorer] = None,
curriculum_checkpoint_dir: str = "checkpoints/curriculum",
max_question_tokens: int = 200,
max_solution_tokens: int = 500,
temperature: float = 0.7,
top_p: float = 0.9,
consensus_temperature: float = 0.7,
device: Optional[torch.device] = None,
unified_accuracy_calc: Optional[Any] = None,
):
# ── Core model attributes (used by generation helpers) ───────────
self.policy = policy_model
self.value = value_model
self.tokenizer = tokenizer
self.max_question_tokens = max_question_tokens
self.max_solution_tokens = max_solution_tokens
self.temperature = temperature
self.top_p = top_p
if device is not None:
self.device = torch.device(device)
else:
try:
self.device = next(policy_model.parameters()).device
except StopIteration:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.reference_questions = reference_questions or []
self.grounded_qa_pairs: List[Dict[str, str]] = [
qa for qa in (grounded_qa_pairs or [])
if qa.get("question") and qa.get("gold_final")
]
self.consensus_temperature = consensus_temperature
self.curriculum_manager = CurriculumManager(checkpoint_dir=curriculum_checkpoint_dir)
self.curriculum_manager.initialize(bootstrap_questions=self.reference_questions)
self.curriculum_manager.load_checkpoint_safe()
self.question_evaluator = QuestionQualityEvaluator(
reference_questions=self.reference_questions
)
# PRM is the sole process-quality signal. Passing prm_scorer=None
# will cause compute_reward/compute_grounded_reward to raise at
# call time — GRPO training always supplies the PRM.
self.prm_scorer = prm_scorer
# Unified accuracy calculator — activated on Phase 2+ transition.
# When use_chain_scoring is True, chain_integrity_score from this
# calculator replaces PRM-based process_score in both grounded and
# self-play reward paths.
self.unified_accuracy_calc: Optional[Any] = unified_accuracy_calc
self.use_chain_scoring: bool = False
self.expert_panel = SimulatedExpertPanel()
self.replay_buffer = GenerationalReplayBuffer(max_size=500)
self.quality_filter = QualityFilter(novelty_threshold=0.5)
self.last_replay_ratio: float = 0.0
self.last_rollout_mix: Dict[str, int] = {
"fresh": 0,
"replay": 0,
"grounded": 0,
}
# Running counts for the most recent grounded batch, so the training
# script can log grounded accuracy per iteration without re-parsing
# trajectory metadata.
self.last_grounded_stats: Dict[str, float] = {
"count": 0,
"correct": 0,
"accuracy": 0.0,
"mean_reward": 0.0,
}
def sample_instruction(self) -> Tuple[str, str, float]:
topic, difficulty = self.curriculum_manager.select_topic_and_difficulty()
instruction = self.curriculum_manager.generate_instruction(
topic=topic, target_difficulty=difficulty
)
return instruction, topic, difficulty
def format_solution_prompt(self, question: str) -> str:
"""Format a question into a chat-templated solver prompt."""
messages = create_solver_messages(question)
return self.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
def format_question_generation_prompt(self, instruction: str) -> str:
"""Format a curriculum instruction into a chat-templated generator prompt."""
messages = create_generator_messages(instruction)
return self.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
def generate_with_logging(
self,
initial_prompt: str,
max_tokens: int,
phase: str,
) -> Tuple[str, List[Transition]]:
"""
Generate text with per-step PPO-grade transition logging.
Used by the PPO-compatible rollout methods (``collect_rollouts``,
``rollout_trajectory``, ``rollout_grounded_trajectory``). The GRPO
training loop uses ``generate_solutions_batched`` instead.
"""
import torch.nn.functional as F # local import to keep top-level clean
prompt_ids = self.tokenizer.encode(
initial_prompt, return_tensors="pt"
).to(self.device)
prompt_length = prompt_ids.shape[1]
prompt_attn = torch.ones_like(prompt_ids)
temperature = float(self.temperature)
do_sample = temperature > 1e-4
eos_id = self.tokenizer.eos_token_id
pad_id = self.tokenizer.pad_token_id or eos_id
gen_kwargs: Dict[str, Any] = dict(
input_ids=prompt_ids,
attention_mask=prompt_attn,
max_new_tokens=max_tokens,
do_sample=do_sample,
use_cache=True,
output_logits=True,
return_dict_in_generate=True,
pad_token_id=pad_id,
eos_token_id=eos_id,
)
if do_sample:
gen_kwargs["temperature"] = max(temperature, 1e-6)
gen_kwargs["top_p"] = float(self.top_p)
with torch.no_grad():
gen_out = self.policy.generate(**gen_kwargs)
full_ids = gen_out.sequences # [1, P + T]
T_gen = int(full_ids.shape[1] - prompt_length)
if T_gen <= 0:
return "", []
raw_logits = torch.stack([lg[0] for lg in gen_out.logits], dim=0).float()
raw_log_probs = F.log_softmax(raw_logits, dim=-1)
sampled_tokens = full_ids[0, prompt_length:]
chosen_log_probs = raw_log_probs.gather(
1, sampled_tokens.unsqueeze(1)
).squeeze(1)
entropies = -(raw_log_probs.exp() * raw_log_probs).sum(dim=-1)
positions = torch.arange(
prompt_length - 1, prompt_length + T_gen - 1, device=self.device
)
full_attn = torch.ones_like(full_ids)
if self.value is not None:
values = self.value.values_at_positions(
input_ids=full_ids, positions=positions, attention_mask=full_attn
)
else:
values = torch.zeros(T_gen, device=self.device)
piece_by_piece: List[str] = self.tokenizer.batch_decode(
[[tok.item()] for tok in sampled_tokens], skip_special_tokens=False
)
transitions: List[Transition] = []
running_text = initial_prompt
for t in range(T_gen):
state_input_ids = full_ids[0, : prompt_length + t]
current_state = State(
text=running_text,
input_ids=state_input_ids,
attention_mask=torch.ones_like(state_input_ids),
phase=phase,
)
action_token = int(sampled_tokens[t].item())
action = Action(
token_id=action_token,
log_prob=float(chosen_log_probs[t].item()),
entropy=float(entropies[t].item()),
)
next_text = running_text + piece_by_piece[t]
next_input_ids = full_ids[0, : prompt_length + t + 1]
next_state = State(
text=next_text,
input_ids=next_input_ids,
attention_mask=torch.ones_like(next_input_ids),
phase=phase,
)
is_done = eos_id is not None and action_token == eos_id
transitions.append(
Transition(
state=current_state,
action=action,
reward=0.0,
next_state=next_state,
value=float(values[t].item()),
done=is_done,
)
)
running_text = next_text
if is_done:
break
generated_ids = full_ids[0, prompt_length : prompt_length + len(transitions)]
generated_text = self.tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
return generated_text, transitions
def _compute_format_score(self, solution: str) -> float:
"""
Structural format score based purely on text patterns — no SymPy.
Checks:
- Presence of 'Step N:' lines (multi-step structure)
- Presence of 'Final Answer:' line (correct termination)
- Length: ≥2 step lines scores highest
Returns a score in [0, 1].
"""
lines = solution.splitlines()
step_lines = [l for l in lines if re.match(r"^\s*Step\s+\d+\s*:", l)]
has_final = any(re.match(r"^\s*Final Answer\s*:", l, re.IGNORECASE) for l in lines)
n_steps = len(step_lines)
if n_steps >= 2:
length_bonus = 1.0
elif n_steps == 1:
length_bonus = 0.5
else:
length_bonus = 0.0
final_ok = 1.0 if has_final else 0.0
# 0.7 × step-structure + 0.3 × final-answer presence
return max(0.0, min(1.0, 0.7 * length_bonus + 0.3 * final_ok))
def compute_reward(
self,
question: str,
solution: str,
target_topic: str,
target_difficulty: float,
) -> Dict[str, object]:
# With a PRM scorer plugged in we skip the expensive (and noisy)
# TripleVerifier consensus step. PRM gives per-step correctness
# against the actual question semantics, which is strictly better
# than "do 3 independent samples agree?"
if self.prm_scorer is not None:
return self._compute_reward_with_prm(
question=question,
solution=solution,
target_topic=target_topic,
target_difficulty=target_difficulty,
)
raise RuntimeError(
"compute_reward called without a PRM scorer. "
"CurriculumMathEnvironment requires prm_scorer to be set. "
"Pass prm_scorer=ProcessRewardScorer(...) at construction time."
)
def _compute_reward_with_prm(
self,
question: str,
solution: str,
target_topic: str,
target_difficulty: float,
) -> Dict[str, object]:
"""
Self-play reward using Qwen2.5-Math-PRM as the semantic-correctness
signal. PRM gives per-step probabilities that each reasoning step
is correct *given the question* — exactly the signal consensus
voting was supposed to approximate but couldn't (three samples
from the same policy agree on wrong answers).
Solution reward (PRM path):
R_sol = 0.45·prm_final + 0.35·prm_mean + 0.20·lccp
R = 0.4·R_q + 0.6·R_sol (then expert-panel modifier)
* ``prm_final`` (final step score) is the strongest predictor of
overall answer correctness.
* ``prm_mean`` provides a smooth gradient over all steps.
* ``lccp`` (Longest Correct Consecutive Prefix) rewards chain
integrity — consecutive correct steps before the first failure.
* The 0.4/0.6 Q/Sol split boosts gradient to question-generation
without starving the solution-correctness signal.
"""
assert self.prm_scorer is not None, "caller must check self.prm_scorer"
prm_result = self.prm_scorer.score_solution(
question=question, solution=solution
)
format_score = self._compute_format_score(solution)
prm_mean = float(prm_result.get("mean_score", 0.0))
prm_min = float(prm_result.get("min_score", 0.0))
prm_final = float(prm_result.get("final_score", 0.0))
prm_num_steps = int(prm_result.get("num_steps", 0))
prm_degraded = bool(prm_result.get("degraded", False))
# If the PRM degraded (empty solution, tokeniser mismatch, truncation),
# the output is effectively unparseable. Prior behavior was to fall
# back on SymPy+format, but the upstream ``base_combined_score`` also
# blends in the question reward — so the policy got a positive signal
# for producing a broken solution as long as the *question* looked
# fine. We now treat a degraded PRM as a hard zero on the solution
# reward; the question reward is gated below so the full combined
# score also collapses.
if prm_degraded or prm_num_steps == 0:
solution_reward = 0.0
_sp_lccp = 0.0
sol_valid = False
_sp_chain_integrity: Optional[float] = None
logger.info(
"PRM degraded (%s); sol_reward set to 0.0 (format=%.2f).",
prm_result.get("degraded_reason", "unknown"),
format_score,
)
else:
# LCCP for self-play: same chain-integrity measure as grounded path
_sp_step_scores = prm_result.get("step_scores", []) or []
if _sp_step_scores:
_first_fail = next(
(i for i, s in enumerate(_sp_step_scores) if s <= 0.5),
len(_sp_step_scores),
)
_sp_lccp = _first_fail / len(_sp_step_scores)
else:
_sp_lccp = 0.0
# Self-play solution: PRM-only reward blending mean, final & chain integrity.
# LCCP anchors the grade to *consecutive* correctness, not just bag-of-steps.
solution_reward = (
0.45 * prm_final
+ 0.35 * prm_mean
+ 0.20 * _sp_lccp
)
# Phase 2+ chain scoring: replace PRM solution blend with unified
# chain integrity + dependency consistency. This also populates the
# question_score from the unified calculator so the Q/Sol weighting
# below uses chain-verified signals instead of PRM proxies.
_sp_chain_integrity = None
if self.use_chain_scoring and self.unified_accuracy_calc is not None:
try:
_sp_report = self.unified_accuracy_calc.compute(
solution=solution,
gold_answer=None,
question=question,
topic=target_topic,
phase="selfplay",
)
solution_reward = _sp_report.composite_accuracy
_sp_chain_integrity = _sp_report.chain_integrity_score
except Exception as _sp_exc:
logger.debug("Unified accuracy calc (self-play) failed: %s", _sp_exc)
sol_valid = True
solution_reward = max(0.0, min(1.0, solution_reward))
question_result = self.question_evaluator.evaluate(
question=question,
solution=solution,
# Synthesize a "consensus-equivalent" dict so the question
# evaluator keeps working unchanged. PRM mean score stands
# in for consensus strength since both are correctness proxies.
consensus_result={
"has_majority": prm_mean >= 0.5,
"consensus_strength": prm_mean,
"primary_matches_majority": prm_mean >= 0.5,
"answer_diversity": 0,
"majority_answer": None,
"primary_answer": None,
},
target_topic=target_topic,
target_difficulty=target_difficulty,
)
question_reward = float(question_result["overall_score"])
# Gate the question-quality bonus on having a parseable solution.
# A great-looking question with a broken solution is not progress
# toward self-improvement — it's the policy gaming whichever
# signal is easier to produce.
effective_question_reward = question_reward if sol_valid else 0.0
# Q/Sol = 0.4/0.6 — see note in compute_reward (non-PRM path).
base_combined_score = (
0.4 * effective_question_reward + 0.6 * solution_reward
)
# Format floor: if the solution structure is broken (<0.5 format),
# cap the overall reward at 0.3 regardless of how much the PRM
# likes the prose. Previously we saw combined=0.83 with
# Format=0.30, i.e. the PRM "approved" an output that didn't have
# parseable Step/Final Answer lines — pure reward hacking.
format_floor_active = format_score < 0.5
format_cap = 0.3 if format_floor_active else 1.0
base_combined_score = min(base_combined_score, format_cap)
# Novelty gate: prevent template-copying reward hacking.
# If the model just generates "John has X apples..." with different numbers,
# n-gram similarity to the reference corpus is high → dataset_novelty is LOW.
# We cap the reward to discourage this without penalising genuinely novel questions.
# < 0.20: near-copy of a training question (template + new variables) → cap 0.35
# > 0.85: completely off-domain (not a real math problem style) → cap 0.55
# [0.20, 0.85]: Goldilocks zone → full reward (novelty_cap = 1.0)
_dataset_novelty = float(
question_result.get("novelty", {}).get("dataset_novelty", 0.5)
if isinstance(question_result.get("novelty"), dict)
else 0.5
)
if _dataset_novelty < 0.20:
_novelty_cap = 0.35
elif _dataset_novelty > 0.85:
_novelty_cap = 0.55
else:
_novelty_cap = 1.0
if _novelty_cap < 1.0:
base_combined_score = min(base_combined_score, _novelty_cap)
logger.debug(
"Novelty gate: dataset_novelty=%.2f → cap=%.2f (was %.3f → now %.3f)",
_dataset_novelty, _novelty_cap,
base_combined_score / _novelty_cap if _novelty_cap > 0 else 0,
base_combined_score,
)
expert_adjustment = self.expert_panel.apply_expert_preferences(
base_reward=base_combined_score,
question_metrics=question_result,
solution_metrics={
# Only format_compliance still influences shaping — the
# PRM/correctness signal lives inside ``solution_reward``
# already and must not be double-counted here.
"format_compliance": format_score,
},
iteration=self.curriculum_manager.current_iteration,
)
combined_score = float(expert_adjustment["adjusted_reward"])
# Re-clip after additive shaping + respect the format cap one more
# time so the shaping can't lift a badly-formatted solution back
# above the cap.
combined_score = max(0.0, min(format_cap, combined_score))
# Curriculum mastery: consider self-play solution "successful" when
# both the chain mean AND the final concluding step are above threshold.
# Using prm_final as a required condition prevents a solution that gets
# most steps right but fails the conclusion from being marked "mastered".
solution_success = (
(not prm_degraded)
and (prm_mean >= 0.65)
and (prm_final >= 0.50)
)
self.curriculum_manager.update_from_trajectory(
topic=target_topic,
question_reward=question_reward,
solution_success=solution_success,
combined_reward=combined_score,
measured_difficulty=float(question_result["measured_difficulty"]),
)
modifier_val = float(expert_adjustment.get("reward_modifier", 0.0))
floor_tag = " FLOOR" if format_floor_active else ""
valid_tag = "" if sol_valid else " [SOL_INVALID]"
logger.info(
"PRM reward%s: combined=%.3f = clip(base=%.3f + mod=%+.3f, cap=%.2f)%s "
"| Q=%.2f sol=%.3f novelty=%.2f | "
"sol=0.45*prm_final(%.2f)+0.35*prm_mean(%.2f)+0.20*lccp(%.2f) "
"| steps=%d",
valid_tag,
combined_score,
base_combined_score,
modifier_val,
format_cap,
floor_tag,
effective_question_reward,
solution_reward,
_dataset_novelty,
prm_final,
prm_mean,
_sp_lccp if sol_valid else 0.0,
prm_num_steps,
)
# Shape a consensus-style verification_details dict so downstream
# aggregation (which reads these keys) keeps working unchanged.
verification_details = {
"consensus": {
"has_majority": prm_mean >= 0.5,
"consensus_strength": prm_mean,
"primary_matches_majority": prm_mean >= 0.5,
"answer_diversity": 0,
"majority_answer": None,
"primary_answer": extract_final_answer_numeric_str(solution) or None,
"prm_mean_score": prm_mean,
"prm_min_score": prm_min,
"prm_final_score": prm_final,
"prm_step_scores": prm_result.get("step_scores", []),
"prm_num_steps": prm_num_steps,
"prm_degraded": prm_degraded,
},
}
return {
"combined_score": combined_score,
"base_combined_score": base_combined_score,
"effective_question_reward": effective_question_reward, # gated (0 when sol invalid)
"question_metrics": question_result,
"solution_metrics": {
"overall_score": solution_reward,
"correctness": prm_mean,
"format_compliance": format_score,
"efficiency": prm_mean, # legacy slot
"consensus_score": prm_mean, # legacy slot
"prm_mean_score": prm_mean,
"prm_min_score": prm_min,
"prm_final_score": prm_final,
"prm_step_scores": prm_result.get("step_scores", []),
"prm_num_steps": prm_num_steps,
"prm_degraded": prm_degraded,
"verification_details": verification_details,
},
"curriculum_metrics": {
"target_topic": target_topic,
"target_difficulty": target_difficulty,
"detected_topic": question_result["detected_topic"],
"measured_difficulty": question_result["measured_difficulty"],
},
"expert_metrics": expert_adjustment,
# Chain scoring metrics (Phase 2+; None when use_chain_scoring=False)
"sp_chain_integrity_score": _sp_chain_integrity,
}
# ------------------------------------------------------------------
# Grounded (GSM8K-anchored) rollouts
# ------------------------------------------------------------------
#
# Why this exists: self-play rewards are dominated by consensus voting
# between 3 same-model samples, which correlates poorly with GSM8K
# accuracy (all three samples can be wrong in the same way). For the
# grounded path we solve a known GSM8K problem and score the solution
# directly against the gold final answer, which is the only signal
# guaranteed to move the benchmark we actually evaluate on.
#
# The reward: R = 0.50·gt_match + 0.40·process(PRM) + 0.10·format
#
# * gt_match = 1.0 iff the model's Final Answer is mathematically
# equivalent to the GSM8K gold final (via sympy.simplify on the
# extracted numeric string).
# * process = 0.60·prm_final + 0.40·prm_mean (PRM step-level quality)
# * format rewards Step N: lines and a Final Answer: line.
#
# No TripleVerifier call on this path — ground truth obviates consensus.
@staticmethod
def _norm_expr_for_match(s: str) -> str:
s = (s or "").strip()
s = s.replace("^", "**")
s = re.sub(r"[,$€£\s]+", "", s)
return s
@classmethod
def _answers_equivalent(cls, pred: str, gold: str) -> bool:
"""Return True iff ``pred`` and ``gold`` parse to the same number."""
if not pred or not gold:
return False
p = cls._norm_expr_for_match(pred)
g = cls._norm_expr_for_match(gold)
if p == g:
return True
try:
diff = simplify(
parse_expr(normalize_for_parse_expr(p))
- parse_expr(normalize_for_parse_expr(g))
)
return bool(diff == 0)
except Exception:
return False
def compute_grounded_reward(
self,
question: str,
solution: str,
gold_final: str,
) -> Dict[str, object]:
"""
Compute a ground-truth-anchored reward for a solution to a known
GSM8K problem. No TripleVerifier call — the gold final answer
replaces consensus voting as the semantic check.
"""
format_score = self._compute_format_score(solution)
pred_final = extract_final_answer_numeric_str(solution) or ""
gt_match_bool = self._answers_equivalent(pred_final, gold_final)
if gt_match_bool:
gt_match = 1.0
else:
# Soft numeric proximity: reward near-misses rather than cliffing at 0.
# Gives partial credit proportional to how close the numeric answer is.
# Capped at 0.85 so an exact match (1.0) is always strictly better.
# Non-numeric wrong answers still get 0.0.
try:
_p = float(pred_final.replace(",", "").strip())
_g = float(gold_final.replace(",", "").strip())
_denom = max(abs(_g), 1.0)
gt_match = min(0.85, 1.0 / (1.0 + 2.0 * abs(_p - _g) / _denom))
except (ValueError, TypeError, AttributeError):
gt_match = 0.0
# Optional PRM step-level quality on grounded rollouts.
# prm_final (last step score) is the strongest single predictor of
# answer correctness. step_accuracy = fraction of steps the PRM
# considers correct — the direct measure of reasoning process quality.
prm_mean = 0.0
prm_final = 0.0
prm_step_scores: List[float] = []
prm_num_steps = 0
prm_degraded = True
if self.prm_scorer is not None:
prm_result = self.prm_scorer.score_solution(
question=question, solution=solution
)
prm_degraded = bool(prm_result.get("degraded", False))
if not prm_degraded:
prm_mean = float(prm_result.get("mean_score", 0.0))
prm_final = float(prm_result.get("final_score", 0.0))
prm_step_scores = list(prm_result.get("step_scores", []))
prm_num_steps = int(prm_result.get("num_steps", 0))
# Step accuracy: fraction of individual steps rated correct by PRM.
step_accuracy = (
sum(1.0 for s in prm_step_scores if s > 0.5) / len(prm_step_scores)
if prm_step_scores else 0.0
)
# Longest Correct Consecutive Prefix (LCCP): fraction of steps from
# the start that are ALL rated correct before the first failure.
# This captures chain integrity — a broken step 3 makes steps 4+ invalid
# regardless of their individual PRM scores.
# LCCP=1.0 means every step was correct (necessary condition for right answer).
# LCCP=0.0 means step 1 itself was wrong (model never had a valid chain).
if prm_step_scores:
first_fail = next(
(i for i, s in enumerate(prm_step_scores) if s <= 0.5), len(prm_step_scores)
)
lccp = first_fail / len(prm_step_scores)
else:
lccp = 0.0
if self.prm_scorer is not None and not prm_degraded:
# process_score: weight prm_final (conclusion step) more than mean
# — the final step is the most critical and most predictive.
process_score = 0.60 * prm_final + 0.40 * prm_mean
combined = (
0.50 * gt_match
+ 0.40 * process_score
+ 0.10 * format_score
)
_gt_tag = "exact" if gt_match_bool else f"prox={gt_match:.2f}"
components_str = (
f"0.50×{gt_match:.2f}({_gt_tag}) + 0.40×proc({process_score:.3f}"
f"[fin={prm_final:.2f},mean={prm_mean:.2f}]) + "
f"0.10×fmt({format_score:.3f})"
)
else:
combined = 0.85 * gt_match + 0.15 * format_score
components_str = (
f"0.85×{gt_match:.2f} + 0.15×fmt({format_score:.3f})"
)
# Phase 2+ chain scoring: override process_score, step_accuracy, lccp,
# and combined with formally-verified chain integrity metrics.
# PRM is still called above so its scores remain logged for comparison.
_chain_report = None
if self.use_chain_scoring and self.unified_accuracy_calc is not None:
try:
_chain_report = self.unified_accuracy_calc.compute(
solution=solution,
gold_answer=gold_final,
topic="grounded",
phase="grounded",
)
process_score = _chain_report.chain_integrity_score
step_accuracy = _chain_report.step_arithmetic_score
lccp = _chain_report.lccp_score
combined = max(0.0, min(1.0,
0.50 * gt_match + 0.30 * process_score + 0.20 * lccp
))
components_str = (
f"0.50×{gt_match:.2f} + 0.30×chain({process_score:.3f}"
f"[arith={_chain_report.step_arithmetic_score:.2f},"
f"dep={_chain_report.step_dependency_score:.2f}]) + "
f"0.20×lccp({lccp:.3f})"
)
except Exception as _chain_exc:
logger.debug("Unified accuracy calc failed, keeping PRM scores: %s", _chain_exc)
else:
combined = max(0.0, min(1.0, combined))
# Hard negative mining: wrong-answer solutions still get a partial signal
# proportional to how far they got before the first error (LCCP).
# This prevents gradient starvation on hard problems where no solution in
# the group is fully correct — the model still learns "longer correct prefix
# is better" rather than receiving zero reward for all K samples.
if gt_match < 0.5 and lccp > 0.0 and self.prm_scorer is not None:
# Bonus = 0.15 × LCCP, capped so that a wrong answer (combined ≈ 0.40)
# can never exceed 0.55 — always well below a correct answer (≈ 0.90+).
_hnm_bonus = 0.15 * lccp
combined = min(combined + _hnm_bonus, 0.55)
_chain_depth = first_fail if prm_step_scores else 0
logger.info(
"Grounded reward: combined=%.3f = %s | pred=%r gold=%r | "
"step_acc=%.0f%% lccp=%.0f%% (chain=%d/%d ok_count=%d) n_steps=%d",
combined,
components_str,
pred_final,
gold_final,
100 * step_accuracy,
100 * lccp,
_chain_depth,
len(prm_step_scores),
sum(1 for s in prm_step_scores if s > 0.5),
prm_num_steps,
)
return {
"combined_score": combined,
"gt_match": gt_match_bool,
# process metrics
"step_accuracy": step_accuracy,
"lccp": lccp, # longest correct consecutive prefix ratio
"prm_mean_score": prm_mean,
"prm_final_score": prm_final,
"prm_step_scores": prm_step_scores,
"prm_num_steps": prm_num_steps,
"prm_degraded": prm_degraded,
# format / answer
"format_score": format_score,
"pred_final": pred_final,
"gold_final": gold_final,
# chain scoring metrics (populated in Phase 2+, None otherwise)
"chain_arith_score": _chain_report.step_arithmetic_score if _chain_report else None,
"chain_dep_score": _chain_report.step_dependency_score if _chain_report else None,
"chain_integrity_score": _chain_report.chain_integrity_score if _chain_report else None,
"first_failure_step": _chain_report.first_failure_step if _chain_report else None,
"final_consistent": _chain_report.final_answer_consistent if _chain_report else None,
}
def rollout_grounded_trajectory(self, qa_pair: Dict[str, str]) -> Trajectory:
"""
Run a rollout on a known GSM8K (question, gold_final) pair.
The policy generates a solution to the real question; reward is
dominated by whether the model's final number matches the gold
final (ground-truth-anchored).
"""
question = str(qa_pair["question"]).strip()
gold_final = str(qa_pair["gold_final"]).strip()
solution_prompt = self.format_solution_prompt(question)
generated_solution, solution_transitions = self.generate_with_logging(
initial_prompt=solution_prompt,
max_tokens=self.max_solution_tokens,
phase="grounded_solution",
)
reward_result = self.compute_grounded_reward(
question=question,
solution=generated_solution,
gold_final=gold_final,
)
terminal_reward = float(reward_result["combined_score"])
trajectory = Trajectory()
for idx, transition in enumerate(solution_transitions):
transition.reward = (
terminal_reward if idx == len(solution_transitions) - 1 else 0.0
)
trajectory.add(transition)
metadata = {
"rollout_source": "grounded",
"curriculum_iteration": self.curriculum_manager.current_iteration,
"target_topic": "grounded_gsm8k",
"target_difficulty": 0.5,
"instruction": "",
"generated_question": question,
"generated_solution": generated_solution,
"question_length": 0,
"solution_length": len(solution_transitions),
"detected_topic": "grounded_gsm8k",
"detected_secondary_topics": [],
"topic_match_score": 1.0,
"estimated_difficulty": 0.5,
"clarity_score": 1.0,
"novelty_scores": {"combined": 0.0},
"consensus_achieved": bool(reward_result["gt_match"]),
"consensus_strength": 1.0 if reward_result["gt_match"] else 0.0,
"answer_diversity": 0,
"majority_answer": None,
"primary_matches_majority": bool(reward_result["gt_match"]),
"question_reward": 0.0,
"solution_reward": terminal_reward,
"pre_expert_reward": terminal_reward,
"expert_reward_modifier": 0.0,
"expert_phase": "grounded",
"expert_feedback": "ground-truth anchored",
"replay_candidate": False,
"replay_novelty": 0.0,
"replay_added": False,
"combined_reward": terminal_reward,
"reward_breakdown": {
"grounded": True,
"gt_match": bool(reward_result["gt_match"]),
"format_score": float(reward_result["format_score"]),
"pred_final": reward_result["pred_final"],
"gold_final": reward_result["gold_final"],
"prm_mean_score": float(reward_result.get("prm_mean_score", 0.0)),
"prm_num_steps": int(reward_result.get("prm_num_steps", 0)),
"prm_step_scores": list(reward_result.get("prm_step_scores", [])),
"prm_degraded": bool(reward_result.get("prm_degraded", True)),
},
"topics_in_sweet_spot": self.curriculum_manager.get_sweet_spot_topics(),
"current_focus_topics": self.curriculum_manager.get_current_focus(),
"curriculum_state_snapshot": self.curriculum_manager.get_curriculum_stats(),
"grounded_gt_match": bool(reward_result["gt_match"]),
"grounded_pred_final": reward_result["pred_final"],
"grounded_gold_final": reward_result["gold_final"],
}
trajectory.metadata = metadata
return trajectory
def rollout_trajectory(self) -> Trajectory:
instruction, target_topic, target_difficulty = self.sample_instruction()
question_prompt = self.format_question_generation_prompt(instruction)
generated_question, question_transitions = self.generate_with_logging(
initial_prompt=question_prompt,
max_tokens=self.max_question_tokens,
phase="question_generation",
)
return self._build_trajectory_from_question(
instruction=instruction,
target_topic=target_topic,
target_difficulty=target_difficulty,
generated_question=generated_question,
question_transitions=question_transitions,
)
def _build_trajectory_from_question(
self,
instruction: str,
target_topic: str,
target_difficulty: float,
generated_question: str,
question_transitions: Optional[List] = None,
) -> Trajectory:
trajectory = Trajectory()
question_transitions = question_transitions or []
solution_prompt = self.format_solution_prompt(generated_question)
generated_solution, solution_transitions = self.generate_with_logging(
initial_prompt=solution_prompt,
max_tokens=self.max_solution_tokens,
phase="solution",
)
reward_result = self.compute_reward(
question=generated_question,
solution=generated_solution,
target_topic=target_topic,
target_difficulty=target_difficulty,
)
terminal_reward = float(reward_result["combined_score"])
all_transitions = question_transitions + solution_transitions
# Terminal-only reward — gae_lambda=1.0 makes A_t = R - V(s_t) for all t.
for idx, transition in enumerate(all_transitions):
transition.reward = (
terminal_reward if idx == len(all_transitions) - 1 else 0.0
)
trajectory.add(transition)
verification = reward_result["solution_metrics"]["verification_details"]
consensus = verification["consensus"]
question_metrics = reward_result["question_metrics"]
metadata = TrajectoryMetadata(
curriculum_iteration=self.curriculum_manager.current_iteration,
target_topic=target_topic,
target_difficulty=target_difficulty,
instruction=instruction,
generated_question=generated_question,
generated_solution=generated_solution,
question_length=len(question_transitions),
solution_length=len(solution_transitions),
detected_topic=str(question_metrics["detected_topic"]["primary_topic"]),
detected_secondary_topics=[
str(x) for x in question_metrics["detected_topic"]["secondary_topics"]
],
topic_match_score=float(question_metrics["topic_match"]),
estimated_difficulty=float(question_metrics["measured_difficulty"]),
clarity_score=float(question_metrics["clarity"]),
novelty_scores=dict(question_metrics["novelty"]),
consensus_achieved=bool(consensus["has_majority"]),
consensus_strength=float(consensus["consensus_strength"]),
answer_diversity=int(consensus["answer_diversity"]),
majority_answer=consensus.get("majority_answer"),
primary_matches_majority=bool(consensus["primary_matches_majority"]),
sympy_verified=True,
steps_total=int(consensus.get("prm_num_steps", 0)),
steps_verified_ok=int(consensus.get("prm_num_steps", 0)),
steps_failed=0,
final_answer_ok=bool(consensus.get("primary_matches_majority", False)),
question_reward=float(question_metrics["overall_score"]),
solution_reward=float(reward_result["solution_metrics"]["overall_score"]),
pre_expert_reward=float(reward_result["base_combined_score"]),
expert_reward_modifier=float(
reward_result["expert_metrics"]["reward_modifier"]
),
expert_phase=str(reward_result["expert_metrics"]["phase"]),
expert_feedback=str(reward_result["expert_metrics"]["feedback"]),
replay_candidate=False,
replay_novelty=0.0,
replay_added=False,
combined_reward=terminal_reward,
reward_breakdown=reward_result,
topics_in_sweet_spot=self.curriculum_manager.get_sweet_spot_topics(),
current_focus_topics=self.curriculum_manager.get_current_focus(),
curriculum_state_snapshot=self.curriculum_manager.get_curriculum_stats(),
)
metadata_dict = asdict(metadata)
trajectory.metadata = metadata_dict
# Replay admission: requires trajectory.metadata to already exist
# because check_novelty reads metadata["generated_question"].
is_candidate, reason = self.quality_filter.meets_replay_criteria(metadata_dict)
metadata_dict["replay_candidate"] = is_candidate
if is_candidate:
novelty_score = self.quality_filter.check_novelty(
trajectory, self.replay_buffer.buffer
)
metadata_dict["replay_novelty"] = float(novelty_score)
if self.quality_filter.is_novel_enough(novelty_score):
quality_score = self.quality_filter.compute_quality_score(metadata_dict)
self.replay_buffer.add_trajectory(
trajectory=trajectory,
metadata=metadata_dict,
iteration=self.curriculum_manager.current_iteration,
quality_score=quality_score,
)
metadata_dict["replay_added"] = True
else:
metadata_dict["replay_added"] = False
else:
metadata_dict["replay_added"] = False
metadata_dict["replay_reject_reason"] = reason
trajectory.metadata = metadata_dict
return trajectory
def _get_adaptive_replay_ratio(self) -> float:
iteration = self.curriculum_manager.current_iteration
if iteration < 3:
return 0.0
if iteration < 5:
return 0.15
buffer_stats = self.replay_buffer.get_buffer_stats(current_iteration=iteration)
buffer_health = float(buffer_stats.get("buffer_health", 0.0))
if buffer_health >= 0.75:
return 0.3
if buffer_health >= 0.6:
return 0.25
return 0.2
def collect_rollouts(
self,
num_trajectories: int,
verbose: bool = True,
grounded_ratio: float = 0.0,
) -> List[Trajectory]:
"""
Generate ``num_trajectories`` episodes in-process on the current
device.
Mix:
* ``grounded_ratio`` of rollouts are GSM8K-anchored (real question,
reward scored against gold final answer). These give the policy
a clean gradient toward benchmark correctness and are also ~3x
faster than self-play rollouts (no TripleVerifier call).
* an adaptive fraction is drawn from the replay buffer when buffer
health is good (self-play only).
* the remainder are fresh self-play rollouts.
"""
if num_trajectories <= 0:
return []
# Defensive .eval() on both policy and value before any generation.
# The first iteration runs rollouts right after model load (HF default
# is .train()). Qwen2.5 has zero dropout so this is currently cosmetic,
# but cheap insurance against any future model swap with stochastic layers.
if self.policy is not None:
self.policy.eval()
if self.value is not None:
self.value.eval()
# Grounded rollouts: only if we actually have QA pairs loaded.
if grounded_ratio > 0.0 and self.grounded_qa_pairs:
num_grounded = int(round(num_trajectories * grounded_ratio))
num_grounded = min(num_grounded, num_trajectories)
else:
num_grounded = 0
num_selfplay = num_trajectories - num_grounded
# Within the self-play half, the existing replay-buffer mix applies.
replay_ratio = self._get_adaptive_replay_ratio()
num_replay = int(num_selfplay * replay_ratio)
num_replay = min(num_replay, len(self.replay_buffer))
num_fresh = max(0, num_selfplay - num_replay)
# ---- Grounded rollouts (GSM8K-anchored) --------------------------
grounded_trajectories: List[Trajectory] = []
grounded_correct = 0
grounded_reward_sum = 0.0
if num_grounded > 0:
qa_sample = random.sample(
self.grounded_qa_pairs,
k=min(num_grounded, len(self.grounded_qa_pairs)),
)
# If we asked for more grounded rollouts than we have distinct
# pairs, pad by re-sampling with replacement.
while len(qa_sample) < num_grounded:
qa_sample.append(random.choice(self.grounded_qa_pairs))
pbar = tqdm(
qa_sample,
desc="Grounded rollouts",
unit="ep",
dynamic_ncols=True,
leave=False,
disable=not verbose,
)
for qa in pbar:
trajectory = self.rollout_grounded_trajectory(qa)
grounded_trajectories.append(trajectory)
r = float(trajectory.metadata.get("combined_reward", 0.0))
grounded_reward_sum += r
if bool(trajectory.metadata.get("grounded_gt_match", False)):
grounded_correct += 1
done = len(grounded_trajectories)
pbar.set_postfix(
acc=f"{grounded_correct / done:.1%}",
reward=f"{grounded_reward_sum / done:+.3f}",
refresh=False,
)
# ---- Fresh self-play rollouts ------------------------------------
fresh_trajectories: List[Trajectory] = []
pbar = tqdm(
range(num_fresh),
desc="Self-play rollouts",
unit="ep",
dynamic_ncols=True,
leave=False,
disable=not verbose,
)
running_reward = 0.0
running_ok = 0
for _ in pbar:
trajectory = self.rollout_trajectory()
trajectory.metadata["rollout_source"] = "fresh"
fresh_trajectories.append(trajectory)
running_reward += float(trajectory.metadata.get("combined_reward", 0.0))
if trajectory.metadata.get("final_answer_ok", False):
running_ok += 1
done = len(fresh_trajectories)
pbar.set_postfix(
reward=f"{running_reward / done:+.3f}",
ok=f"{running_ok}/{done}",
refresh=False,
)
# ---- Replay buffer draws -----------------------------------------
replay_trajectories = self.replay_buffer.sample_replay_batch(
num_replay, diversity_sample=True
)
for trajectory in replay_trajectories:
trajectory.metadata["rollout_source"] = "replay"
trajectories = (
grounded_trajectories + fresh_trajectories + replay_trajectories
)
random.shuffle(trajectories)
self.last_replay_ratio = replay_ratio
self.last_rollout_mix = {
"fresh": len(fresh_trajectories),
"replay": len(replay_trajectories),
"grounded": len(grounded_trajectories),
}
grounded_count = len(grounded_trajectories)
self.last_grounded_stats = {
"count": grounded_count,
"correct": grounded_correct,
"accuracy": (
grounded_correct / grounded_count if grounded_count > 0 else 0.0
),
"mean_reward": (
grounded_reward_sum / grounded_count if grounded_count > 0 else 0.0
),
}
if verbose:
buffer_stats = self.replay_buffer.get_buffer_stats(
current_iteration=self.curriculum_manager.current_iteration
)
logger.info(
"Rollout mix: %d grounded + %d fresh + %d replay "
"(grounded_ratio=%.2f, replay_ratio=%.2f, buffer_size=%d, health=%.3f)",
len(grounded_trajectories),
len(fresh_trajectories),
len(replay_trajectories),
grounded_ratio,
replay_ratio,
len(self.replay_buffer),
float(buffer_stats.get("buffer_health", 0.0)),
)
if grounded_count > 0:
logger.info(
"Grounded accuracy this iter: %d/%d = %.1f%% (mean reward %.3f)",
grounded_correct,
grounded_count,
100.0 * grounded_correct / grounded_count,
grounded_reward_sum / grounded_count,
)
self.curriculum_manager.increment_iteration()
self.curriculum_manager.save_state(
iteration=self.curriculum_manager.current_iteration, rollout=None
)
return trajectories
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