Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning

• URL: http://arxiv.org/abs/2505.20561v1
• Date: Mon, 26 May 2025 22:51:00 GMT
• Title: Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning
• Authors: Shenao Zhang, Yaqing Wang, Yinxiao Liu, Tianqi Liu, Peter Grabowski, Eugene Ie, Zhaoran Wang, Yunxuan Li,
• Abstract summary: We recast reflective exploration within the Bayes-Adaptive RL framework.<n>Our resulting algorithm, BARL, instructs the LLM to stitch and switch strategies based on observed outcomes.
• Score: 55.36978389831446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) trained via Reinforcement Learning (RL) have exhibited strong reasoning capabilities and emergent reflective behaviors, such as backtracking and error correction. However, conventional Markovian RL confines exploration to the training phase to learn an optimal deterministic policy and depends on the history contexts only through the current state. Therefore, it remains unclear whether reflective reasoning will emerge during Markovian RL training, or why they are beneficial at test time. To remedy this, we recast reflective exploration within the Bayes-Adaptive RL framework, which explicitly optimizes the expected return under a posterior distribution over Markov decision processes. This Bayesian formulation inherently incentivizes both reward-maximizing exploitation and information-gathering exploration via belief updates. Our resulting algorithm, BARL, instructs the LLM to stitch and switch strategies based on the observed outcomes, offering principled guidance on when and how the model should reflectively explore. Empirical results on both synthetic and mathematical reasoning tasks demonstrate that BARL outperforms standard Markovian RL approaches at test time, achieving superior token efficiency with improved exploration effectiveness. Our code is available at https://github.com/shenao-zhang/BARL.

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