New Paper Demystifies RL Post-Training: Random Rewards Don't Boost LLM Capabilities
natashajaques · x · 2026-09-13
Natasha Jaques and colleagues published "Demystifying Reinforcement Learning Post-Training of Language Models" (arXiv:2608.24949), using careful toy experiments to deconstruct how RL post-training works for LLMs, framed as a tutorial for practitioners.
Key findings:
- RL post-training on random (spurious) rewards does not improve model capabilities except under particular circumstances; the effect depends on the post-training prompt distribution.
- Yet RL post-training can teach capabilities not present in the base model's pass@k distribution — it isn't just selection of existing behaviors.
- The paper isolates how the base model's prior distribution, reward granularity, prompt diversity, and scale shape RL outcomes, using policy entropy as a lens to compare pretraining, SFT, and RL stages.
- It links RL success to whether the base model already places sufficient probability mass on desired behavior, connecting to classical exploration/exploitation theory.
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