Self-Training for Language Models

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11 papers in the last four weeks, up 83% on the four weeks before. 0.1% of all new papers.

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Confidence-Orchestrated Self-Evolution against Uncertain LLM Feedback
May 27, 2026Bowen Wei, Nan Wang, Yuqing Zhou +2Self-Training for Language Models RL for Language Model Reasoning
George Mason University · Amazon
Model Collapse as Cultural Evolution
May 21, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuCultural Evolution Self-Training for Language Models
The University of Hong Kong · Hong Kong, China · Stellaris AI Limited
Self-Policy Distillation via Capability-Selective Subspace Projection
May 21, 2026Guangya Hao, Yitong Shang, Yunbo Long +2Self-Training for Language Models Language Model Distillation
University of Cambridge · 2HKUST · University of Chicago
Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
May 20, 2026Tony Lee, Percy LiangSelf-Training for Language Models Language Model Self-Assessment
Stanford University
Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies
May 20, 2026Ming LiuSelf-Training for Language Models Model Collapse
Amazon
Query-Conditioned Test-Time Self-Training for Large Language Models
May 13, 2026Chaehee Song, Minseok Seo, Yeeun Seong +2Test-Time Adaptation Test-Time Optimization
School of Electrical Engineering, KAIST · 2Graduate School of Green Growth and Sustainability, KAIST
EvoLM: Self-Evolving Language Models through Co-Evolved Discriminative Rubrics
May 5, 2026Shuyue Stella Li, Rui Xin, Teng Xiao +8RL for Language Models LLM Evaluation
University of Washington · 2Allen Institute for AI · University of Pennsylvania
STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering
Apr 19, 2026Wei Chen, Lili Zhao, Zhi Zheng +2Multi-Hop QA Self-Training for Language Models
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei, Anhui, China · NIO Hefei, Anhui, China
Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning
Apr 19, 2026Zhiyin Yu, Bo Zhang, Qibin Hou +3Self-Training for Language Models RL for Language Model Reasoning
Peking University · Shanghai Artificial Intelligence Laboratory · Nankai University +1
Peer-Predictive Self-Training for Language Model Reasoning
Apr 14, 2026Shi Feng, Hanlin Zhang, Fan Nie +2Self-Training for Language Models Language Model Post-Training
Harvard University · Stanford University
A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning
Oct 21, 2025Mengqi Li, Lei Zhao, Anthony Man-Cho So +2Self-Training for Language Models Mathematical Reasoning
The Chinese University of Hong Kong, Shenzhen · Shanghai Jiao Tong University · The Chinese University of Hong Kong

May 27, 2026, cs.AIJ/K move · Enter open · S save

Confidence-Orchestrated Self-Evolution against Uncertain LLM Feedback

Bowen Wei, Nan Wang, Yuqing Zhou, Jinhao Pan+1

George Mason University · Amazon

Self-Training for Language Models RL for Language Model Reasoning Proximal Policy Optimization Language Model Self-Improvement

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Self-evolving large language models (LLMs) learn by generating their own training tasks and solutions, reducing reliance on human-curated supervision. However, in many reasoning domains, the model must also validate generated tasks and judge generated answers to obtain training signals. This creates a training-signal challenge: erroneous self-judgments become erroneous gradient updates. Existing approaches either rely on external verifiers, which limits generality, or treat noisy self-generated feedback as supervision. We propose COSE (Confidence-Orchestrated Self-Evolution), which uses the LLM's intrinsic confidence as a lightweight uncertainty signal to modulate learning. COSE introduces confidence-weighted PPO updates and confidence-prioritized replay. Across 19 held-out benchmarks and four Qwen/Llama backbones (0.6B--4B), COSE consistently improves over base models and achieves the best average performance in general reasoning and mathematics, while remaining competitive on code. Code and data are available at https://anonymous.4open.science/r/COSE_-B5C2.

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