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bf9e424 | 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 | """Generate a MolForge SFT JSONL dataset with rare-action coverage.
Most records come from the deterministic team policy so the examples are
grounded in real environment trajectories. A smaller coverage slice is added
for rare but valid schema variants such as defer, each assay tool, and edit
subtypes so SFT teaches the model the whole action surface.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from pathlib import Path
from typing import Iterable
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from inference_common import ( # noqa: E402
SYSTEM_PROMPT,
MolForgeAction,
MolForgeObservation,
attach_reasoning_fields,
attach_team_messages,
build_model_payload,
heuristic_team_action,
)
from scenarios import DEFAULT_TOOL_COSTS # noqa: E402
from server.molforge_environment import MolForgeEnvironment # noqa: E402
def main() -> None:
parser = argparse.ArgumentParser(description="Generate MolForge all-action SFT JSONL.")
parser.add_argument("--episodes", type=int, default=90)
parser.add_argument("--max-turns", type=int, default=10)
parser.add_argument("--output", default="data/molforge_sft_all_actions.jsonl")
parser.add_argument(
"--randomized",
action="store_true",
help="Enable MolForge training randomization while collecting policy traces.",
)
args = parser.parse_args()
if args.randomized:
os.environ["MOLFORGE_TRAINING_RANDOMIZATION"] = "1"
output_path = Path(args.output)
output_path.parent.mkdir(parents=True, exist_ok=True)
env = MolForgeEnvironment()
records = []
for _ in range(args.episodes):
observation = env.reset()
for _ in range(args.max_turns):
if observation.done:
break
action = heuristic_team_action(observation)
records.append(make_record(observation, action, source="policy_trace"))
observation = env.step(action)
for observation, action in curated_coverage_examples():
action = attach_reasoning_fields(observation, action)
action = attach_team_messages(observation, action)
records.append(make_record(observation, action, source="coverage_example"))
with output_path.open("w", encoding="utf-8") as handle:
for record in records:
handle.write(json.dumps(record, ensure_ascii=True) + "\n")
print(
json.dumps(
{
"output": str(output_path),
"records": len(records),
"coverage_records": sum(
1 for record in records if record["metadata"]["source"] == "coverage_example"
),
},
indent=2,
)
)
def curated_coverage_examples() -> Iterable[tuple[MolForgeObservation, MolForgeAction]]:
env = MolForgeEnvironment()
observations = [env.reset(), env.reset(), env.reset()]
for observation in observations:
yield observation, MolForgeAction(
action_type="defer",
acting_role="lead_chemist",
rationale="Hold this turn because the team needs a cleaner evidence-backed move.",
)
easy, medium, hard = observations
yield easy, MolForgeAction(
action_type="edit",
acting_role="lead_chemist",
edit_type="add_fragment",
slot="back_pocket",
fragment="cyano",
rationale="Add a compact cyano handle to improve potency without large lipophilic risk.",
)
yield medium, MolForgeAction(
action_type="edit",
acting_role="lead_chemist",
edit_type="remove",
slot="back_pocket",
rationale="Remove the risky back-pocket group and return to a simpler default handle.",
)
yield hard, MolForgeAction(
action_type="edit",
acting_role="lead_chemist",
edit_type="undo_last_edit",
slot="solvent_tail",
rationale="Undo the last tail change when the visible evidence suggests it raised risk.",
)
for observation in observations:
for tool_name in DEFAULT_TOOL_COSTS:
yield observation, MolForgeAction(
action_type="run_assay",
acting_role="assay_planner",
tool_name=tool_name,
rationale=f"Run {tool_name} to close a visible evidence gap before committing.",
)
yield hard, MolForgeAction(
action_type="restart",
acting_role="lead_chemist",
rationale="Restart early because the hard scenario starts in a trap series.",
)
yield easy, MolForgeAction(
action_type="submit",
acting_role="lead_chemist",
rationale="Submit only when visible evidence is sufficient and budget should be preserved.",
)
def make_record(
observation: MolForgeObservation,
action: MolForgeAction,
*,
source: str,
) -> dict[str, object]:
return {
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": json.dumps(
build_model_payload(observation, compact=False),
separators=(",", ":"),
),
},
{
"role": "assistant",
"content": json.dumps(
action.model_dump(exclude_none=True),
separators=(",", ":"),
),
},
],
"metadata": {
"source": source,
"scenario_id": observation.scenario_id,
"difficulty": observation.difficulty,
"step_index": observation.step_index,
"action_type": action.action_type,
},
}
if __name__ == "__main__":
main()
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