@inproceedings{du-etal-2026-escaping,
title = "Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn {LLM} Self-Reflection",
author = "Du, Linxuan and
Xue, Guangquan and
Liang, Xiaobo and
Huang, Qipeng and
Ding, Yuyang and
Shi, Xinyu and
Yijun, Zhang and
Qi, Ji and
Zhu, Wenpeng and
Li, Juntao and
Zhang, Min",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1636/",
pages = "35393--35405",
ISBN = "979-8-89176-390-6",
abstract = "Despite the potential of multi-turn self-reflection to improve LLM reasoning, its effectiveness in practice is severely constrained by a failure mode we term the Echo Trap.Specifically, this phenomenon gives rise to two coupled problems: (1) the model becomes limited by its inherent capabilities and tends to repeat earlier reflections to preserve reward signals; (2) once such ``copy'' behavior is reinforced, the model ceases to try new strategies, leading to exploration collapse.We attribute this issue to imprecise credit assignment during training, as standard GRPO assigns rewards at the trajectory level, making it difficult to distinguish which reflection steps contribute to improved outcomes.To address this limitation, we propose a tree-structured extension of GRPO for multi-turn self-reflection, which enables more accurate advantage estimation.Through extensive experiments, we analyze the Echo Trap and demonstrate that our method effectively mitigates behavior collapse and improves performance across multiple benchmarks."
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<abstract>Despite the potential of multi-turn self-reflection to improve LLM reasoning, its effectiveness in practice is severely constrained by a failure mode we term the Echo Trap.Specifically, this phenomenon gives rise to two coupled problems: (1) the model becomes limited by its inherent capabilities and tends to repeat earlier reflections to preserve reward signals; (2) once such “copy” behavior is reinforced, the model ceases to try new strategies, leading to exploration collapse.We attribute this issue to imprecise credit assignment during training, as standard GRPO assigns rewards at the trajectory level, making it difficult to distinguish which reflection steps contribute to improved outcomes.To address this limitation, we propose a tree-structured extension of GRPO for multi-turn self-reflection, which enables more accurate advantage estimation.Through extensive experiments, we analyze the Echo Trap and demonstrate that our method effectively mitigates behavior collapse and improves performance across multiple benchmarks.</abstract>
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%0 Conference Proceedings
%T Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection
%A Du, Linxuan
%A Xue, Guangquan
%A Liang, Xiaobo
%A Huang, Qipeng
%A Ding, Yuyang
%A Shi, Xinyu
%A Yijun, Zhang
%A Qi, Ji
%A Zhu, Wenpeng
%A Li, Juntao
%A Zhang, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F du-etal-2026-escaping
%X Despite the potential of multi-turn self-reflection to improve LLM reasoning, its effectiveness in practice is severely constrained by a failure mode we term the Echo Trap.Specifically, this phenomenon gives rise to two coupled problems: (1) the model becomes limited by its inherent capabilities and tends to repeat earlier reflections to preserve reward signals; (2) once such “copy” behavior is reinforced, the model ceases to try new strategies, leading to exploration collapse.We attribute this issue to imprecise credit assignment during training, as standard GRPO assigns rewards at the trajectory level, making it difficult to distinguish which reflection steps contribute to improved outcomes.To address this limitation, we propose a tree-structured extension of GRPO for multi-turn self-reflection, which enables more accurate advantage estimation.Through extensive experiments, we analyze the Echo Trap and demonstrate that our method effectively mitigates behavior collapse and improves performance across multiple benchmarks.
%U https://aclanthology.org/2026.acl-long.1636/
%P 35393-35405
Markdown (Informal)
[Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection](https://aclanthology.org/2026.acl-long.1636/) (Du et al., ACL 2026)
ACL
- Linxuan Du, Guangquan Xue, Xiaobo Liang, Qipeng Huang, Yuyang Ding, Xinyu Shi, Zhang Yijun, Ji Qi, Wenpeng Zhu, Juntao Li, and Min Zhang. 2026. Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 35393–35405, San Diego, California, United States. Association for Computational Linguistics.