Calibrating the Evaluator: Does Probability Calibration Mitigate Preference Coupling in LLM Agent Feedback Loops?
Researchers tackle a critical failure mode in LLM agent training: evaluator bias systematically corrupts learned behaviors when agents optimize against flawed feedback. This work tests whether probability calibration on evaluator judgments can break the feedback loop, using DeepSeek-V4-Pro and GLM5.2 in controlled experiments. The finding matters because production agent systems increasingly rely on LLM-based reward signals, and unchecked evaluator drift compounds across training iterations. If calibration proves effective, it offers a practical lever for stabilizing agent learning without retraining evaluators from scratch.
Modelwire context
ExplainerThe paper isolates evaluator bias as a distinct failure mode from model capability gaps, testing whether statistical recalibration of feedback signals (rather than retraining evaluators) can interrupt the corruption loop. This is narrower than it sounds: it assumes the evaluator's underlying judgments are sound but miscalibrated, not fundamentally wrong.
This connects directly to the Visual Semantic Entropy work from late June, which exposed how overconfident model outputs suppress uncertainty signals during decoding. Both papers identify a calibration failure in the feedback layer itself rather than the primary model. The current work extends that insight to agent training loops, where miscalibrated evaluator confidence compounds across iterations. The Hard-Routed MoR-LoRA paper from the same period also emphasizes preserving unit-scale assumptions in composed modules, suggesting the field is converging on calibration preservation as a design principle across adaptation methods.
If DeepSeek-V4-Pro and GLM5.2 show sustained performance gains on held-out agent tasks after calibration, but those gains vanish when the evaluator is swapped for a different LLM (even a stronger one), that confirms the fix is brittle and calibration-specific rather than addressing a fundamental agent learning problem. Watch for ablations showing whether calibration helps equally across different evaluator architectures.
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MentionsDeepSeek-V4-Pro · GLM5.2 · TTRL · EPC
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