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Non-myopic Generation of Language Models for Reasoning and Planning
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Large Language Models have demonstrated remarkable abilities in reasoning and planning by breaking down complex problems into sequential steps. Despite their success in various domains like mathematical problem-solving and coding, LLMs face challenges in ensuring reliable and optimal planning due to their inherent myopic nature of autoregressive decoding. This paper revisits LLM reasoning from an optimal-control perspective, proposing a novel method, Predictive-Decoding, that leverages Model Predictive Control to enhance planning accuracy. By re-weighting LLM distributions based on foresight trajectories, Predictive-Decoding aims to mitigate early errors and promote non-myopic planning. Our experiments show significant improvements in a wide range of tasks for math, coding, and agents. Furthermore, Predictive-Decoding demonstrates computational efficiency, outperforming search baselines with reduced computational resources. This study provides insights into optimizing LLM planning capabilities.
Forward citations
Cited by 5 Pith papers
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DeepLook: Deeper Thinking with Lookahead
A training-free decoder that branches only at low-confidence reasoning segments and ranks continuations by average lookahead confidence reports equal or better accuracy at roughly 1/8 of a confidence-filtered baseline...
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Dual-Dimensional Consistency: Balancing Budget and Quality in Adaptive Inference-Time Scaling
DDC reduces token consumption by over 10x in LLM reasoning while maintaining or exceeding baseline accuracy across five benchmarks via adaptive path quality filtering.
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MUR: Momentum Uncertainty guided Reasoning for Large Language Models
MUR selectively applies test-time scaling to steps whose uncertainty exceeds a momentum-smoothed history, saving tokens and often modestly improving accuracy.
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POSS: Position Specialist Generates Better Draft for Speculative Decoding
Using position-specialized draft layers instead of one single draft model improves later-token acceptance in speculative decoding, yielding modest speedups on Llama-3-8B and Llama-2-13B.
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VeriThinker: Learning to Verify Makes Reasoning Model Efficient
VeriThinker shows that fine-tuning a reasoning model only on a solution-verification task reduces chain-of-thought length on MATH500 and AIME by 20-45% while preserving or slightly improving accuracy.
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