Feedback That Backfires: Why Small Language Model Agents Repeat the Call They Just Watched Fail
Agent harnesses record a failed tool call and its error message in the transcript and ask the model to continue, on the assumption that the error is corrective information. We measure whether it is. Defining the corrective gain of a failure record as the change in log-probability of re-emitting the action that just failed, we find the gain is negative for every instruction-tuned model we tested (6 checkpoints, 135M-1.7B, 4 families) in two environments: simulated tool calling and MBPP program repair. Normalised by action length the effect is about -1.03 nats per action token, a factor of 2.8 in the odds of each token, and holds on 90%-100% of individual items, not only on average. Over a ...
| Authors | Esmail Gumaan |
|---|---|
| Primary category | cs.SE |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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