Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback

Jul 16, 2026

A new preprint introduces a FLOP-accounting framework for reinforcement learning (RL) post-training, breaking down compute usage into rollout/search, policy-update/learning, and reward-model evaluation. The study, using LoRA-adapted Qwen2.5 policies, finds that optimal allocation of compute resources depends on factors such as model size, compute budget, and reward system. The authors also propose RACE, a diagnostic protocol to help identify effective compute allocation regimes before committing to expensive validation runs.

Why it matters: This work offers a systematic approach to allocating limited post-training compute in RL, which is important for efficiently adapting foundation models.

Full story at: arXiv Machine Learning ↗