Spike 002a — Trace Collection via TRL + OpenEnv

Risk: MEDIUM. Validates whether TRL's GRPOTrainer + OpenEnv environment registry produce clean, schema-stable trace JSONL. Status: 📋 planned (depends on 001 verdict) Comparison spike: runs head-to-head with 002b-trace-collection-prime-rl/.

Question (Given / When / Then)

Given Qwen3-7B base + TRL GRPOTrainer + a SWE-bench-lite OpenEnv, when we run 100 rollouts, then all rollouts emit complete (state_t, action_t, reward_t) tuples to JSONL with no truncation or schema drift, and the JSONL is loadable by spike 003 without preprocessing.

Approach (TBD)

Set up a minimal TRL GRPOTrainer config pointing at SWE-bench-lite as an OpenEnv environment.
Run 100 rollouts, capturing trace tuples to traces.jsonl.
Verify schema, count truncations, count missing reward signals.

Why this risk-tier

If the trace stream is dirty (missing fields, schema drift mid-rollout, truncated states), spike 003 (DPO-pair extraction) gets nothing useful. But this risk is medium not high because both TRL and OpenEnv are well-tested upstream — the fail mode is integration glue, not feasibility.

Files (planned)

setup.py — TRL + verifiers + transformers + accelerate install
train_config.py — minimal GRPOConfig
run_rollout.py — collect 100 rollouts to traces.jsonl
validate_schema.py — schema check + completeness stats
traces.jsonl (gitignored — large; uploaded to dataset repo)
verdict.md — final verdict

Hardware

1× A100 80GB on Modal (per modal-llm-training skill)
Wallclock estimate: ~4–8 hours for 100 rollouts (depends on rollout length)

Blocked on

Spike 001 verdict. If 001 fails, this spike is moot.