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From Roots to Rewards: Dynamic Tree Reasoning with RL

Created by
  • Haebom

Author

Ahmed Bahloul, Simon Malberg

Outline

This paper presents a novel framework based on dynamic reinforcement learning to address the fixed inference tree and over-estimation of all possible solution strategies in the existing Probabilistic Tree-of-Thought (ProbTree) framework. The framework incrementally builds an inference tree based on real-time confidence estimation and learns optimal policies for action selection, such as decomposition, search, or aggregation. It improves both solution quality and computational efficiency through selective expansion and intensive resource allocation while maintaining the probabilistic strictness of ProbTree. As a result, we present a new tree-based inference paradigm that balances the reliability of probabilistic frameworks with the flexibility required for practical question-answering systems.

Takeaways, Limitations

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Takeaways:
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We effectively solved the problem of fixed inference tree and excessive computational cost of existing ProbTree using dynamic reinforcement learning.
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We dynamically build inference trees based on real-time confidence estimation to simultaneously improve solution quality and computational efficiency.
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We present a novel inference paradigm that combines the reliability of probabilistic frameworks with the flexibility of real-world question-answering systems.
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Limitations:
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Further experiments and analysis are needed to determine the actual performance and generalization ability of the proposed framework.
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There is a lack of detailed description and analysis of the learning process in dynamic reinforcement learning.
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Further evaluation of applicability and performance on different types of questions and datasets is needed.
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