Language Models can Self-Improve at State-Value Estimation for Better
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Authors: name: Ethan Mendes, name: Alan Ritter

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.