Let LLMs Wander: Engineering RL Environments
summary
Claims that verifiable RL beats SFT get concrete here: the open library Verifiers builds Python environments wired to OpenAI‑compatible endpoints and tool calls, synthetic SFT of 200 synthetic examples fixes format errors, then GRPO/CISPO style group RL trains LFM‑2 into a robust tic‑tac‑toe player that draws 85% vs an optimal opponent. Practical fixes include stratified batching, seeding opponents to remove noise, opponent skill annealing (random‑move prob 0–70% then 0–25%), and careful batch size tuning to avoid collapse.
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chapters (9)
- 0:00Introduction to RL for LLMs
- 2:11RL and LLM training concepts
- 5:56Verifiable rewards and mapping
- 10:12Building with Verifiers
- 11:52Environment implementation patterns
- 18:52Designing a Tic-Tac-Toe environment
- 25:42Training and optimization process
- 30:53Evaluation and iteration results
- 35:29Lessons and future directions
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