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LiteSearch: Efficacious Tree Search for LLM
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Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree Search) can dramatically boost LLM performance on complex mathematical reasoning tasks. However, they often require more than 10 times the computational resources of greedy decoding due to wasteful search strategies, making them difficult to be deployed in practical applications. This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget (maximum number of children) calculation to tackle this issue. By considering the search progress towards the final answer (history) and the guidance from a value network (future) trained without any step-wise annotations, our algorithm iteratively selects the most promising tree node before expanding it within the boundaries of the allocated computational budget. Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach not only offers competitive performance but also enjoys significantly lower computational costs compared to baseline methods.
Forward citations
Cited by 5 Pith papers
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Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning
A plan-then-reason SFT plus a plan-quality reward in GRPO improves math-reasoning accuracy by small but consistent margins over GRPO and DAPO.
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CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Adding actor perplexity and multi-head critic variance as intrinsic exploration bonuses improves RLVR math reasoning accuracy by roughly +2 to +3 points on AIME benchmarks.
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Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
A two-stage, value-guided decoding strategy with a margin-based reward adjustment is claimed to yield more faithful, detailed VLM captions at about a quarter of VisVM's inference cost.
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PATS: Process-Level Adaptive Thinking Mode Switching
PATS adapts the number of beam-search candidates per reasoning step using process reward model scores, improving accuracy-efficiency tradeoffs on math benchmarks.
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Reward Model Generalization for Compute-Aware Test-Time Reasoning
The paper claims PRM generalization error controls test-time compute efficiency and that the proposed CATS controller improves accuracy on MATH-500 and AIME24.
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