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| """Pick GRPO training tasks from a probe run (baseline_eval.py --split train --samples N). | |
| python make_curriculum.py probe_train_l1.jsonl -o curriculum_l1.json | |
| GRPO learns only from groups whose rewards differ. A task the policy never gets past compiling gives an | |
| all-zero group, so keep tasks where some sample was correct, or at least ran end to end (wrong output | |
| still earns partial credit, so its group has reward differences). | |
| """ | |
| import argparse | |
| import json | |
| from collections import defaultdict | |
| from baseline_eval import STAGE_ORDER | |
| KEEP_FROM = STAGE_ORDER.index("correctness") # ran to completion; "timed" = correct | |
| def choose(episodes: list) -> tuple: | |
| best = defaultdict(int) # task -> furthest stage index over all samples and turns | |
| for ep in episodes: | |
| for t in ep["turns"]: | |
| best[ep["task_id"]] = max(best[ep["task_id"]], STAGE_ORDER.index(t["stage"])) | |
| keep = sorted(t for t, s in best.items() if s >= KEEP_FROM) | |
| solved = sorted(t for t, s in best.items() if s == STAGE_ORDER.index("timed")) | |
| return keep, solved, len(best) | |
| def main(): | |
| p = argparse.ArgumentParser() | |
| p.add_argument("probe") | |
| p.add_argument("-o", "--out", default="curriculum.json") | |
| args = p.parse_args() | |
| eps = [json.loads(line) for line in open(args.probe) if line.strip()] | |
| keep, solved, total = choose(eps) | |
| with open(args.out, "w") as f: | |
| json.dump(keep, f, indent=1) | |
| print(f"{len(keep)}/{total} tasks kept ({len(solved)} solved at least once) -> {args.out}") | |
| if __name__ == "__main__": | |
| main() | |