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arXiv Machine Learning By Naoki Nishikawa, Taiji Suzuki

Reinforcement Learning for Hierarchical Reasoning Rewards: Minimax-Optimal Rates with Transformers

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The paper studies reinforcement learning (RL) for post‑training language models on reasoning tasks, focusing on a hierarchical reward structure where each component becomes relevant only after the previous ones are resolved. It demonstrates that a Transformer‑based actor–critic algorithm that alternates between KL‑regularized policy sampling, critic fitting, and policy updates achieves minimax‑optimal rates in query budget and regularization strength, and is optimal for a fixed number of prompts. In contrast, sampling from a fixed reference distribution, as used in offline reward modeling, only yields a logarithmic regret decay, highlighting the advantage of on‑policy exploration in concentrating on high‑reward regions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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