arXiv:2606.07190v1 Announce Type: new Abstract: Reasoning prefixes shape the future trajectory of LLM problem solving, yet existing process reward models usually evaluate them through local step correctness. We argue that correctness is a useful but indirect proxy for the effect we ultimately care about: whether a prefix increases the probability of successful completion. We define this effect as prefix gain, the solve-rate improvement induced by conditioning lightweight student model group on a prefix, and use it to train a Prefix Utility Model (PUM) with a simple pairwise ranking objective.
The rapid advancement and deployment of LLMs necessitate more effective and efficient methods for guiding their problem-solving trajectories, moving beyond simple correctness measures.
This research provides a more sophisticated framework for evaluating and improving LLM reasoning, potentially leading to significant gains in the reliability and autonomy of AI agents.
The focus for evaluating LLM prefixes shifts from local step correctness to their overall utility in achieving successful task completion, enabling more robust fine-tuning and development.
- · AI developers
- · LLM fine-tuners
- · AI agent designers
- · Enterprises deploying AI
- · Developers relying solely on correctness metrics
- · Less efficient LLM training methods
Improved performance and reliability of complex LLM-driven applications and AI agents.
Accelerated development of autonomous AI systems capable of tackling more open-ended problems.
Increased trust and adoption of AI in critical infrastructure and decision-making processes as their reasoning becomes more robust.
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Read at arXiv cs.CL