Re-feeding Is Not Replaying: Measuring Replay Noise in Counterfactual Token-Credit Estimation
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Computer Science > Machine Learning
arXiv:2606.15621 (cs)
[Submitted on 14 Jun 2026]
Title:Re-feeding Is Not Replaying: Measuring Replay Noise in Counterfactual Token-Credit Estimation
Authors:Nils Matteson
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Abstract:Per-token counterfactual credit estimation asks which token in a language-model rollout caused the final answer to be right or wrong: cut the transcript at a pivot, substitute an alternative token, replay continuations, and compare outcomes. Published methods re-feed the transcript prefix as a fresh prompt, assuming this reproduces the state the model passed through during generation. We measure what that assumption costs on a stock inference engine, with a three-pass design: continuations resumed from the verified decode-time KV state, an identical second exact pass (a replica noise floor), and a re-feed pass. Across six configurations and three models (including a GRPO-trained checkpoint), at low-margin decision tokens, re-feeding changes the credit estimate at rates 14-28 percentage points above the replica floor (7-21pp under a treatment-independent conditioning; problem-clustered t = 2.9-6.4). Most changes are zero-boundary crossings of the quantized estimator rather than polarity reversals, and the perturbation is consistent with mean-zero, so averaged quantities are largely safe; but selection is not: a critical-token set chosen by thresholding $|\hat{A}_t|$ under re-feed overlaps the exact-resume selection at Jaccard 0.34-0.90, versus a 0.63-0.96 replica ceiling. A causal confirmation closes the loop: under vLLM's batch-invariant kernels all three passes are identical on every measured channel, with both disagreement rates exactly zero. Replica passes themselves disagree on 9-23% of eligible estimates: single-sample credit measurements at decision tokens are unreliable under any replay. Settings were fixed in advance; exact-pass cache hits in the second campaign are instrumented (100% hit rate, 3,434 pivots); total compute was under 10 USD. We recommend that counterfactual credit studies resume decoder state or use batch-invariant kernels, and report a replica floor.
| Comments: | 10 pages, 3 figures. Code, per-pivot data, logs, and registration: this https URL (benchmarks/, paper/refeed-drift/) |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.15621 [cs.LG] |
| (or arXiv:2606.15621v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.15621
arXiv-issued DOI via DataCite (pending registration)
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