# Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents

DevFeed: [Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents](<https://devfeed.tech/articles/neglected-free-lunch-from-post-training-progress-advantage-for-llm-agents-116725.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2606.26080>)

Author: Changdae Oh, Wendi Li, Seongheon Park, Samuel Yeh, Tanwi Mallick, Sharon Li

Published: 2026-11-04T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [llm-reasoning](<https://devfeed.tech/topics/llm-reasoning.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [annotation](<https://devfeed.tech/tags/annotation.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [failure](<https://devfeed.tech/tags/failure.md>), [llm](<https://devfeed.tech/tags/llm.md>), [markov](<https://devfeed.tech/tags/markov.md>), [monte-carlo](<https://devfeed.tech/tags/monte-carlo.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [real-world](<https://devfeed.tech/tags/real-world.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [reward](<https://devfeed.tech/tags/reward.md>), [scaling](<https://devfeed.tech/tags/scaling.md>), [uncertainty-quantification](<https://devfeed.tech/tags/uncertainty-quantification.md>)

## AI overview

The paper proposes progress advantage as an annotation-free, step-level signal for evaluating LLM agents, derived from RL post-training rather than a separately trained process reward model. It claims that, in a general stochastic Markov decision process, the log-probability ratio between an RL-trained policy and its reference policy recovers the optimal advantage function. The authors report tests across test-time scaling, uncertainty quantification, and failure attribution on five benchmarks and four model families; in those settings, the signal outperformed confidence-based baselines and dedicated reward models. The supplied evidence is an abstract and does not provide implementation details or individual benchmark results.

## Source excerpt

arXiv:2606.26080v2 Announce Type: replace Abstract: Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irreversible actions, and stochastic environment feedback make both human annotation and Monte Carlo estimation infeasible at scale. In this work, we show that reinforcement learning (RL) post-training already provides the ingredients for effective step-level scoring, eliminating the need for dedicated reward model training altogether. Concretely, we derive an implicit advantage under a general stochastic Markov decision process, which we term progress advantage -- log-probability ratio between the RL-trained policy and its reference policy exactly recovers the optimal advantage function. This formulation makes the resulting signal annotation-free, domain-agnostic, and available as a byproduct of the standard RL post-training pipeline. We validate the effectiveness of the progress advantage across three different applications: test-time scaling, uncertainty quantification, and failure attribution on five benchmarks and four model families. Across all settings, it consistently outperforms confidence-based baselines and, despite requiring no task-specific training, surpasses dedicated trained reward models. We complement these results with deeper analyses on characteristics of progress advantage, offering practical guidance for adoption in real-world agentic systems.