Re-ReST: Reflection-Reinforced Self-Training for Language Agents

Challenge:	Existing methods to fine tune language agents with reasoning-action trajectories require high-quality model-generated samples, which are hard to obtain for challenging language agent tasks.
Approach:	They propose a method to employ reflection during inference without ground-truth feedback to improve agents more autonomously.
Outcome:	The proposed method improves self-training performance on open-source language agents by 7.6% and 14.1% respectively.

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Overview

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Approach

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Dialogue Systems & Conversational Agents

Challenge:	Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be prohibitive in terms of feasibility, time, and resources.
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Green & Sustainable NLP

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SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection (2025.emnlp-main)

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Yubin Ge, Salvatore Romeo, Jason Cai, Monica Sunkara, Yi Zhang

Language Models

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ReCreate: Reasoning and Creating Domain Agents Driven by Experience (2026.acl-long)

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Zhezheng Hao, Hong Wang, Jian Luo, Jianqing Zhang, Yuyan Zhou, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen

Reasoning

Challenge:	Large Language Model (LLM) agents are reshaping the industrial landscape, but tasks differ widely, making them labor-intensive to build.
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ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning (2026.findings-acl)

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Juyong Jiang, Jiasi Shen, Sunghun Kim, Kang Min Yoo, Jeonghoon Kim, Sungju Kim

Language Models

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Contextual Experience Replay for Self-Improvement of Language Agents (2025.acl-long)

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Yitao Liu, Chenglei Si, Karthik R Narasimhan, Shunyu Yao

Green & Sustainable NLP

Challenge:	Existing large language model (LLM) agents fail in complex tasks without any environment-specific experiences.
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Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction (2025.naacl-long)

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Liping Liu, Chunhong Zhang, Likang Wu, Chuang Zhao, Zheng Hu, Ming He, Jianping Fan

Language Models

Challenge:	Existing approaches involve models iterating and improving their previous responses based on internal reflection ability or external feedback.
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Improving Retrospective Language Agents via Joint Policy Gradient Optimization (2025.naacl-long)

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Xueyang Feng, Bo Lan, Quanyu Dai, Lei Wang, Jiakai Tang, Xu Chen, Zhenhua Dong, Ji-Rong Wen

Language Models

Challenge:	Recent advances in large language models have sparked interest in creating autonomous agents.
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PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)

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Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

Natural Language Interfaces

Challenge:	Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models.
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