Co-rewarding：無標註穩定RL誘導LLM推理

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🧠閱讀原文： 机器之心

#self-supervised-rl#reward-hacking#llm-reasoningco-rewarding

💡ICLR 2026 breakthrough: stable label-free RL for LLMs, beats reward hacking.

⚡ 30-Second TL;DR

有什麼變化

ICLR 2026接收Co-rewarding自監督RL框架

為什麼重要

實現無昂貴標註的RL訓練擴展推理，潛在加速研究人員與開發者面對數據短缺的LLM進展。

下一步行動

Read the paper at https://openreview.net/forum?id=fDk95XPsCU and test Co-rewarding on your LLM RL pipeline.

誰應關注：Researchers & Academics

關鍵要點

•ICLR 2026接收Co-rewarding自監督RL框架
•透過數據/模型視角互補訊號消除RLVR標註需求
•防止自獎勵LLM的訓練崩潰與獎勵駭客
•誘導大語言模型穩定推理能力
•提供論文與程式碼連結供複現

🧠 深度解析

背景與延伸：來自公開資料，非原文內容。引用 6 個來源。

🔑 增強重點摘要

•Co-rewarding is a self-supervised RL framework for LLM reasoning, accepted to ICLR 2026, developed by researchers from Hong Kong Baptist University and Shanghai Jiao Tong University.
•It addresses RLVR annotation bottlenecks and self-rewarding failures by using complementary self-supervised signals from data and model views, eliminating the need for ground-truth labels.
•The framework prevents reward hacking and training collapse, inducing stable reasoning capabilities in large language models.
•ICLR 2026 features 5344 accepted papers from 18949 submissions (28.20% acceptance rate), highlighting competitive selection for works like Co-rewarding[1].
•Related ICLR 2026 papers tackle similar reward hacking issues in RL for LLMs and diffusion models, indicating active research in stable RL paradigms[2][4][5].

📊 競品分析▸ Show

Method	Key Feature	Benchmarks
Co-rewarding	Label-free self-supervised RL with complementary signals	Prevents collapse in LLM reasoning (no specific benchmarks in results)
REA-RL	Reflection-aware online RL for efficient reasoning models	ICLR 2026 acceptance [2]
SMORM	Joint Bradley-Terry and multi-objective reward modeling	Outperforms 70B baseline with 7B model in OOD settings [4]
IntDiff	Intrinsic rewards for diffusion model fine-tuning	Improves alignment and diversity in text-to-image [5]
LongR	Contextual dense rewards for long-context reasoning	~4% gain over outcome-only baselines [3]

🛠️ 技術深入

•Co-rewarding employs complementary self-supervised signals from data/model perspectives to stabilize RL training without labels, targeting reward hacking prevention in LLM reasoning induction.
•No specific model architecture or implementation details (e.g., code links) found in search results beyond original article mention.
•Related works: LongR uses Relative Information Gain (white-box metric) and interleaved Think-and-Read policy with curriculum learning for long-context RLVR[3].
•REA-RL provides GitHub implementation for reflection-aware online RL, accepted to ICLR 2026[2].

🔮 前景展望AI analysis grounded in cited sources

Co-rewarding advances label-free RL for LLMs, potentially reducing annotation costs and improving reasoning stability amid growing ICLR focus on reward modeling robustness, enabling scalable self-improvement in reasoning models.

📎 來源 (6)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

🧠閱讀原文： 机器之心

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