Why Does Feedback Augmented Self Distillation Fail

# Why Does Feedback-Augmented Self-Distillation Fail to Improve Retrieval-Interleaved Search Agents?

Derived from arXiv:2607.17558 - Why Does Feedback-Augmented Self-Distillation Fail to Improve Retrieval-Interleaved Search Agents?

## Core Concept
On-policy self-distillation (OPSD) offers a promising approach for training large language models without relying on a separate teacher model. However, its effectiveness on complex agentic tasks remains largely unexplored. In this work, we instantiate Feedback-Augmented Self-Distillation (FA-SD), a self-distillation algorithm for agentic search that leverages successful demonstrations as privileged information. We identify that models can rely on recurring reasoning-and-search output templates, ...

## Key Insights
- Derived from arXiv:2607.17558
- Published: 2026-07-20
- Utility Score: 1.00
- Authors: Fan Yang, Rui Meng, Yuxin Wen

## Activation
why-does-feedback-augmented-self-distillation-fail, 2607.17558

## References
- arXiv: https://arxiv.org/abs/2607.17558