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About this paper
Chenghao Yang, Lin Gui, Chenxiao Yang, Victor Veitch, Lizhu Zhang, Zhuokai Zhao
Exploratory Annealed Decoding (EAD) introduces a lightweight sampling strategy for Reinforcement Learning with Verifiable Rewards (RLVR) in Large Language Models, dynamically adjusting sampling temperature across token generation. This method mitigates entropy collapse and improves sample efficiency, yielding enhanced training stability and superior inference-time performance across various RLVR algorithms.
Key insights
- The utility of exploration is not uniform across token generation steps; early tokens critically establish semantic direction, while later tokens require coherence for quality.
- Fixed-temperature sampling struggles to provide an optimal balance, as high temperatures degrade quality and low temperatures inhibit crucial exploration.
- The principle of 'explore at the beginning, exploit at the end' can effectively address both sample quality and training stability challenges in autoregressive LLM generation.
Summary by alphaXiv.
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