From Agent Failures to Text Policies: What Works and What Breaks
TextGrad improves language-model systems by revising text from feedback. Its core thesis is that natural-language feedback can act as a gradient for optimizing text components without changing model weights. Applying it to agents is harder because feedback arrives only after a sequence of actions, making it difficult to identify which decision caused failure. We study this problem by separating the ability to follow a useful policy from the ability to learn that policy from experience. Our main finding is a clear gap between these two abilities. Human-written policies improve two frozen 7B agents on TextWorldExpress by 5.0 success points, showing that useful policy text exists. However, p...
| Authors | Jaideep Ray, Ankit Goyal |
|---|---|
| Primary category | cs.CL |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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