Language Models can Self-Improve at State-Value Estimation for Better Search

Ethan Mendes, Alan Ritter

Language Models can Self-Improve at State-Value Estimation for Better Search: 10 upvotes on Hugging Face Daily Papers, #15 of 26 papers on 2025-03-05. Day-by-day upvote history.

Collecting ground truth task completion rewards or human demonstrations for multi-step reasoning tasks is often cost-prohibitive and time-consuming, especially in interactive domains like web tasks. To address this bottleneck, we present self-taught lookahead, a self-supervised method that leverages state-transition dynamics to train a value model capable of effectively guiding language model-controlled search. We find that moderately sized (8 billion parameters) open-weight value models improved with self-taught lookahead can match the performance of using a frontier LLM such as gpt-4o as the value model. Furthermore, we find that self-taught lookahead improves performance by 20% while reducing costs 37x compared to previous LLM-based tree search, without relying on ground truth rewards.

Paper page on Hugging Face · arXiv

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