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632c145 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | """Scenario grouping and adaptive curriculum helpers for GRPO training."""
from __future__ import annotations
import random
import threading
from collections.abc import Iterable, Mapping, Sequence
from dataclasses import dataclass, field
from typing import Any
@dataclass
class AdaptiveDifficultyCurriculum:
min_level: int = 0
max_level: int = 3
current_level: int = 0
promote_after: int = 50
promote_threshold: float = 0.70
demote_threshold: float = 0.35
ema_alpha: float = 0.10
rng_seed: int = 0
counts: dict[int, int] = field(default_factory=dict)
ema_success: dict[int, float] = field(default_factory=dict)
def __post_init__(self) -> None:
self.min_level = int(self.min_level)
self.max_level = int(self.max_level)
self.current_level = max(self.min_level, min(int(self.current_level), self.max_level))
self._rng = random.Random(int(self.rng_seed))
def sample_difficulty(self, available_difficulties: Iterable[int]) -> int:
available = {int(item) for item in available_difficulties}
if not available:
raise ValueError("No cached difficulties are available for GRPO curriculum sampling.")
candidates = [
max(self.min_level, self.current_level - 1),
self.current_level,
min(self.max_level, self.current_level + 1),
]
weights = [0.20, 0.65, 0.15]
weighted: dict[int, float] = {}
for level, weight in zip(candidates, weights):
if level in available:
weighted[level] = weighted.get(level, 0.0) + weight
if not weighted:
nearest = min(available, key=lambda level: (abs(level - self.current_level), level))
return nearest
levels = list(weighted)
return int(self._rng.choices(levels, weights=[weighted[level] for level in levels], k=1)[0])
def update(self, difficulty: int, success: float | bool) -> dict[str, Any]:
level = int(difficulty)
value = max(0.0, min(1.0, float(success)))
self.counts[level] = self.counts.get(level, 0) + 1
old = self.ema_success.get(level, 0.0)
self.ema_success[level] = (1.0 - self.ema_alpha) * old + self.ema_alpha * value
if level == self.current_level and self.counts[level] >= self.promote_after:
if self.ema_success[level] >= self.promote_threshold:
self.current_level = min(self.max_level, self.current_level + 1)
elif self.ema_success[level] <= self.demote_threshold:
self.current_level = max(self.min_level, self.current_level - 1)
return self.snapshot()
def snapshot(self) -> dict[str, Any]:
return {
"current_level": self.current_level,
"counts": {str(key): value for key, value in sorted(self.counts.items())},
"ema_success": {
str(key): value for key, value in sorted(self.ema_success.items())
},
"current_level_ema_success": self.ema_success.get(self.current_level, 0.0),
}
class ScenarioGroupRegistry:
"""Assign each GRPO group to exactly one cached scenario."""
def __init__(
self,
entries: Sequence[Mapping[str, Any]],
*,
split: str = "train",
initial_difficulty: int = 0,
rng_seed: int = 0,
max_level: int | None = None,
) -> None:
self.split = split
self._rng = random.Random(int(rng_seed))
self._lock = threading.Lock()
self._assignments: dict[int, dict[str, Any]] = {}
self._completed_groups: set[int] = set()
self._entries_by_difficulty: dict[int, list[dict[str, Any]]] = {}
self._cursors: dict[int, int] = {}
for entry in entries:
if entry.get("validated") is not True or entry.get("split") != split:
continue
difficulty = int(entry.get("difficulty", 0))
self._entries_by_difficulty.setdefault(difficulty, []).append(dict(entry))
for difficulty, items in self._entries_by_difficulty.items():
items.sort(key=lambda item: (int(item.get("seed", 0)), str(item.get("scenario_hash", ""))))
self._rng.shuffle(items)
self._cursors[difficulty] = 0
if not self._entries_by_difficulty:
raise ValueError(f"No validated cached scenarios are available for split={split!r}.")
available = sorted(self._entries_by_difficulty)
resolved_max = max_level if max_level is not None else max(available)
self.curriculum = AdaptiveDifficultyCurriculum(
min_level=min(available),
max_level=int(resolved_max),
current_level=int(initial_difficulty),
rng_seed=int(rng_seed),
)
@property
def available_difficulties(self) -> list[int]:
return sorted(self._entries_by_difficulty)
def assignment_for(
self,
*,
scenario_group_id: int,
requested_seed: int | None = None,
requested_difficulty: int | None = None,
split: str | None = None,
difficulty_policy: str = "adaptive",
) -> dict[str, Any]:
group_id = int(scenario_group_id)
with self._lock:
if group_id in self._assignments:
return dict(self._assignments[group_id])
if difficulty_policy == "fixed":
difficulty = int(
requested_difficulty
if requested_difficulty is not None
else self.curriculum.current_level
)
entry = self._find_entry(
seed=requested_seed,
split=split or self.split,
difficulty=difficulty,
) or self._next_entry(difficulty)
else:
difficulty = self.curriculum.sample_difficulty(self.available_difficulties)
entry = self._next_entry(difficulty)
assignment = self._assignment_from_entry(group_id, entry)
self._assignments[group_id] = assignment
return dict(assignment)
def record_group_outcome(self, scenario_group_id: int, success_rate: float) -> dict[str, Any] | None:
group_id = int(scenario_group_id)
with self._lock:
if group_id in self._completed_groups:
return None
self._completed_groups.add(group_id)
assignment = self._assignments.get(group_id)
if not assignment:
return self.curriculum.snapshot()
return self.curriculum.update(
int(assignment["difficulty"]),
max(0.0, min(1.0, float(success_rate))),
)
def metrics(
self,
records: Sequence[Mapping[str, Any]],
*,
unique_trace_count: int,
duplicate_trace_suppressed_count: int,
) -> dict[str, float]:
scenario_hashes = {
str(record.get("scenario_hash") or record.get("scenario_id_hash") or "")
for record in records
if record.get("scenario_hash") or record.get("scenario_id_hash")
}
seeds = {
int(record.get("scenario/seed", record.get("seed", 0)) or 0)
for record in records
}
total = max(1, len(records))
snapshot = self.curriculum.snapshot()
return {
"train/unique_trace_count": float(unique_trace_count),
"train/duplicate_trace_suppressed_count": float(duplicate_trace_suppressed_count),
"train/unique_trace_rate": float(unique_trace_count) / total,
"train/unique_seed_count": float(len(seeds)),
"train/unique_scenario_hash_count": float(len(scenario_hashes)),
"train/curriculum_level": float(snapshot["current_level"]),
"train/curriculum_ema_success": float(snapshot["current_level_ema_success"]),
}
def _find_entry(
self,
*,
seed: int | None,
split: str,
difficulty: int,
) -> dict[str, Any] | None:
if seed is None or split != self.split:
return None
for entry in self._entries_by_difficulty.get(int(difficulty), []):
if int(entry.get("seed", -1)) == int(seed):
return dict(entry)
return None
def _next_entry(self, difficulty: int) -> dict[str, Any]:
level = int(difficulty)
items = self._entries_by_difficulty.get(level)
if not items:
nearest = min(
self.available_difficulties,
key=lambda item: (abs(item - level), item),
)
items = self._entries_by_difficulty[nearest]
level = nearest
cursor = self._cursors.get(level, 0)
self._cursors[level] = cursor + 1
return dict(items[cursor % len(items)])
def _assignment_from_entry(self, group_id: int, entry: Mapping[str, Any]) -> dict[str, Any]:
cache_key = entry.get("cache_key") if isinstance(entry.get("cache_key"), Mapping) else {}
return {
"scenario_group_id": int(group_id),
"seed": int(entry.get("seed", 0)),
"split": str(entry.get("split", self.split)),
"difficulty": int(entry.get("difficulty", 0)),
"scenario_hash": str(entry.get("scenario_hash", "")),
"template_id": str(entry.get("template_id") or cache_key.get("app_family", "")),
"bug_family": str(entry.get("bug_family") or cache_key.get("authz_bug_type", "")),
}
def build_scenario_group_rows(
*,
dataset_size: int,
training_prompt: str,
seed_start: int = 0,
split: str = "train",
difficulty: int = 0,
difficulty_policy: str = "adaptive",
) -> list[dict[str, Any]]:
return [
{
"prompt": [{"role": "user", "content": training_prompt}],
"scenario_group_id": int(seed_start) + index,
"seed": int(seed_start) + index,
"difficulty": int(difficulty),
"split": split,
"difficulty_policy": difficulty_policy,
}
for index in range(int(dataset_size))
]
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