titleCan an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents
authorsAshutosh Hathidara
date2026-10-13
venuearXiv (preprint, not peer reviewed)
idarXiv:2610.18614
affiliationSAP
upvotes6 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractControlling an acting LLM agent means deciding, at each step of a long-horizon task, whether to trust its next action, abstain, or hand off to a human. That decision needs a per-step confidence signal predicting whether the current rollout will eventually succeed. The agent's own self-reported verbalized confidence is the natural candidate, but existing reinforcement learning methods calibrate it against the correctness of a decision at the current step. We propose a method to calibrate it against the eventual outcome of a multi-step episode: under GRPO, the group of rollouts for a task supplies success labels, and a proper scoring rule scores each step's confidence against the realized outcome. In offline RL on recorded trajectories of a Qwen2.5-7B agent in ALFWorld, TextWorld, and WebShop environments, the AUROC of step confidence against episode outcome rises from near chance to 0.68-0.85, and the top-10% of steps the agent is most confident about succeed ~4.3x more often than average. In online RL on the live ALFWorld agent, four calibration rewards improve early-step AUROC, expected calibration error goes from 0.42 to below 0.12, and selective execution improves over both the untrained agent and a format-only RL control, without degrading task success. We also introduce an evaluation protocol that separates episode depth, task difficulty, and attempt-level foresight, which shows where the gain lies: the trained agent recognizes from its first step which tasks it will fail, while telling a succeeding attempt from a failing one at the same task is harder, with some early-window signal emerging on TextWorld.

cite

@misc{hathidara2026can,
  title={Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents},
  author={Ashutosh Hathidara},
  year={2026},
  eprint={2610.18614},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2610.18614}
}