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This paper proposes Atom-Searcher, a novel approach that overcomes the Limitations of Augmented Information Retrieval Generation (RAG) to enhance the complex problem-solving ability of large-scale language models (LLMs). To address the limitations of outcome-based reinforcement learning (conflicting gradients and reward sparsity) faced by existing agent-based deep learning approaches, we utilize the Inference Reward Model (RRM), which decomposes the inference process into fine-grained functional units (Atomic Thoughts) and provides rewards (Atomic Thought Rewards, ATR) for each unit. Atom-Searcher accelerates convergence to efficient inference paths through a curriculum-based reward schedule. Through seven benchmark experiments, it outperforms existing state-of-the-art methods and demonstrates advantages such as computational scalability during testing, provision of supervision criteria for RRM, and interpretable and human-like inference patterns.
Takeaways, Limitations
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Takeaways:
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A novel approach to overcome the limitations of reinforcement learning in agent-based deep learning research (Atomic Thought, ATR)
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Introducing a curriculum-based reward schedule for efficient inference path learning.
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Ensuring scalability of test time calculations
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Interpretable and human-like reasoning processes
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Improved performance over previous best-in-class benchmarks
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Limitations:
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Further verification of the generalization performance of the proposed method is needed.
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Research on applicability and scalability to various types of problems is needed.
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Possible lack of detailed description of the design and training of the inference reward model (RRM).
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It is difficult to clearly distinguish whether the performance improvement of Atom-Searcher is due to the effect of ATR or other factors.
