Free Lunch for LLMs Confidence: The RL Critic Already Knows

LLM agents operate in stateful environments, where a single erroneous step can waste limited interaction budget or cause irreversible effects before task failure becomes apparent. Reliable deployment therefore requires step-level confidence estimation: estimating, before execution, the probability that a proposed action will advance the task. Existing LLM confidence estimators are typically designed for static question answering under a fixed task context and evaluation criterion. For an agent, however, its action productivity depends on an environment transition that is observed only after execution. To address this challenge, we introduce Critic Experience Bank (CEB), a training-free framework that turns feedback from completed trajectories into reusable evidence for future confidence judgments. After each trajectory, an LLM assigns hindsight productivity pseudo-labels to individual actions and stores them with the critic's original pre-execution confidence, task context, action, and observed feedback. For a new action, by retrieving related productive and unproductive experiences, CEB grounds pre-execution confidence in feedback from completed trajectories to condition a fixed LLM critic. CEB thereby adapts over a task stream without parameter updates or ground-truth step labels at deployment. Across four agent benchmarks spanning offline and live web navigation, mobile GUI and shell tasks, and three critic backbones, CEB achieves the best or tied-best ECE, Brier score, and AUC in all twelve benchmark-backbone settings under rule-based step labels, reducing ECE by up to 53.8% relative to the strongest training-free baseline. Its confidence scores also improve downstream utility in selective execution and simulated task success.

Confidence estimation is crucial for developing trustworthy large language models (LLMs), with most methods following estimator-based or verbalization-based paradigms. While recent research increasingly focuses on improving verbalized self-reports of confidence, we challenge the prevailing view that this approach surpasses independent confidence estimators. Our empirical study shows that a dedicated confidence estimator can substantially outperform verbalized confidence, indicating that LLMs' internal representations contain richer confidence signals. Building on this finding, we propose Iterative Policy-Estimator Training (IPoET), a framework that synergizes the complementary strengths of verbalized reasoning traces and informative representations. IPoET alternates policy optimization with estimator updating, integrating estimator-derived confidence feedback into policy learning and refreshing the estimator on new policy rollouts. Experiments across diverse datasets and Qwen and Llama backbones demonstrate that, by iteratively exploiting richer hidden features and adapting to the evolving policy distribution, IPoET consistently outperforms both estimator- and verbalization-based baselines in-domain and achieves superior or comparable results across all out-of-domain metrics. For more details, refer to https://github.com/xyk829/ipoet.

Reliable self-assessment is essential for large language models (LLMs), yet they often remain highly confident when their answers are wrong. We study concurrent confidence calibration, where confidence is learned alongside capability improvement rather than calibrated only after training. Reinforcement learning from verifiable rewards (RLVR) provides a natural setting for this paradigm, as it continuously produces responses paired with verifiable correctness feedback. Existing concurrent methods, however, learn both capability and confidence through reinforcement learning within shared policy parameters, potentially coupling two fundamentally different learning problems. We instead propose shared experience but separate learning: capability and confidence learn from the same trajectories, but through separate optimization mechanisms and parameters. Based on this principle, we introduce CoCal (Companion Confidence Calibration), which trains a lightweight companion from rollout hidden states and verifier-derived correctness supervision while leaving task optimization unchanged. Experiments on Qwen3-8B and Qwen3-14B show that CoCal improves confidence estimation without sacrificing task performance, outperforming both RL-based concurrent methods and matched post-hoc calibration. The learned companion further generalizes across domains and policy shifts, while the benefits of CoCal persist at both scales.