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Learning to Reason as Action Abstractions with Scalable Mid-Training RL
Created by
Haebom
Author
Shenao Zhang, Donghan Yu, Yihao Feng, Bowen Jin, Zhaoran Wang, John Peebles, Zirui Wang
Outline
Large-scale language models demonstrate strengths in reinforcement learning (RL), but require intermediate training phases to fully utilize their potential. This paper theoretically analyzes the impact of intermediate training on subsequent training and highlights the importance of the action abstraction space for efficient action selection. Based on this, we propose the Reasoning as Action Abstractions (RA3) algorithm, which utilizes sequential variation lower bounds to discover temporally coherent latent structures and fine-tune them based on bootstrapped data. RA3 has been experimentally demonstrated to improve performance on code generation tasks.
Takeaways, Limitations
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Takeaways:
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We theoretically demonstrate that intermediate training steps are crucial for improving the performance of RL-based large-scale language models.
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We suggest that training in an action abstraction space is effective.
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Achieved performance improvement over existing methods in code generation tasks through the RA3 algorithm.
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Limitations:
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The theoretical analysis and effectiveness of the RA3 algorithm are limited to code generation tasks and require generalization to other fields.
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Additional details on the implementation details and hyperparameters of the RA3 algorithm may be lacking.
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Further research is needed on efficient learning and optimization of action abstractions.
