# AttnSR preprint reports improved agent success with fewer training interactions

_Published Friday, November 6, 2026 at 5:26 PM EST · AI, Science · Latest · Tier 2 — Notable_

Researchers report that AttnSR, a method for assigning training rewards to individual agent steps, raised final held-out success on ALFWorld from 91.2% to 97.1% in a controlled Qwen2.5-7B-Instruct study. The preprint also reports improved ScienceWorld performance at every checkpoint while reducing environment interactions by 29.9%.

The method traces model attention backward during training replay to estimate which earlier steps contributed to the final response. It requires neither a separately trained reward model nor additional trial runs. An ablation experiment attributes the benefit to how intermediate rewards are assigned, rather than simply accumulating rewards over time.

## Sources

- [cs.LG updates on arXiv.org](https://arxiv.org/abs/2611.07932)

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Canonical: https://techandbusiness.org/newswire/wpt95YBu16zjZqedUnVdn1
Published: 2026-11-06T22:26:24.597Z
Story chronology: 2026-11-06T05:00:00.000Z
Retrieved: 2026-11-07T01:08:57.080Z
Publisher: Tech & Business (techandbusiness.org)