[2608.19760] Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay
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arXiv:2608.19760 (cs)
[Submitted on 20 Aug 2026]
Title:Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay
Authors:Haiyue Zhang<br>View a PDF of the paper titled Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay, by Haiyue Zhang
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Abstract:Audited against causal ground truth from executed replay in a single-agent tool environment (ALFWorld), none of the step-level credit signals used to train LLM agents -- LLM-judge scores, outcome-conditioned logprob ratios, or the policy's own confidence -- identifies which steps causally matter better than chance. Existing evaluations grade these signals against annotated step *correctness*; we audit them against step *contribution* -- what re-sampling the policy's own alternatives at each decision point and rolling forward actually changes about the outcome -- and the two come apart. The ground truth itself is structured: causal contribution is sparse (30.5% of decision points where ground truth is defined carry measurable effect), and measurability is model-dependent -- the fraction of points with no policy-supported counterfactual differs by a factor of two (13.1% vs. 26.8%) between two similar-scale policies. The failure mode is identifiable: implicit credit echoes the policy's fluency (median rank correlation +0.75, replicating at +0.70 in a second family under a corrected instrument), while conditioning on the outcome adds no causal information (partial correlation -0.004, Qwen). A confidence-only router recovers pivotal steps at chance level, but cuts judge cost by 13.1% per turn (14.0% per trajectory). In a seven-arm pre-registered training experiment, no arm reliably outperforms the untrained policy, and the checkpoints' apparent instrument signature is fully explained by training dose -- sparser credit retains fewer examples, an order-of-magnitude spread in optimizer steps -- not credit content. Comparisons of credit rules must therefore match effective sample size, or they measure dose, not credit.
Comments:<br>49 pages, 7 figures. Pre-registered; frozen analysis plans and prompts included in the appendices. Under review
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as:<br>arXiv:2608.19760 [cs.LG]
(or<br>arXiv:2608.19760v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.19760
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arXiv-issued DOI via DataCite (pending registration)
Submission history<br>From: Haiyue Zhang [view email]<br>[v1]<br>Thu, 20 Aug 2026 08:04:00 UTC (199 KB)
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