arXiv Machine Learning June 26, 2026 By Lecheng Yan, Ruizhe Li, Guanhua Chen, Qing Li, Jiahui Geng, Wenxi Li, Longyue Wang, Chenyang Lyu

Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs

Read the original on arXiv Machine Learning →

arXiv:2601. 11061v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is highly effective for enhancing LLM reasoning, yet recent evidence shows models like Qwen 2.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Sep 15

What Does an LLM Learn from Reinforcement Learning? A Mechanistic Interpretability Perspective with Fixed-SAE Track

arXiv:2609.15064v1 Announce Type: new Abstract: Reinforcement learning (RL) is widely utilized in large language model training to improve targeted capabilities, yet how RL reshapes a model remains p...

By Lingheng Du, Yiming Tang, Xufeng Duan, Dianbo Liu

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 18

Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging

arXiv:2606. 18521v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful post-training paradigm that surpasses Supervised Fine-Tuning (SFT) in eliciting reasoning intelligence and resisting catastrophic forgetting.

By Chenrui Wu, Zexi Li, Jiajun Bu, Jiangchuan Liu, Haishuai Wang

reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 14

SCOPE-RL: Optimizing Reasoning Paths Before and After Success

arXiv:2607. 11506v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards.

By Xiaojian Liu, Han Xu, Jianqiang Xia, Zhixuan Li, Ke Xu, Yiwei Dai, Xinran Chen, Changwo Wu, Yuchen Li

llms reinforcement-learning

arXiv Computer Vision

Aug 27

Boosting Reasoning in Large Multimodal Models via Activation Replay

The paper introduces Activation Replay, a training‑free method that improves reasoning in post‑trained large multimodal models (LMMs) by replaying low‑entropy activations from the base model’s input context. It shows that Reinforcement Learning with Verifiable Rewards (RLVR) shifts low‑entropy activations and that modulating these activations enhances reasoning across tasks such as mathematics, visual agents, and video reasoning. Experiments demonstrate that Activation Replay outperforms alternatives like high‑entropy replay or direct cross‑model intervention, boosting Pass@K and broadening RLVR’s reasoning coverage.

By Yun Xing, Xiaobin Hu, Qingdong He, Jiangning Zhang, Shuicheng Yan, Shijian Lu, Yu-Gang Jiang

agents reinforcement-learning robotics multimodal

arXiv AI

Jul 24

When RLVR Shrinks the Reasoning Boundary: Diagnosing Pass@k Inversion

arXiv:2607. 20543v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) can improve one-sample accuracy while making a model worse under repeated sampling.

By Todd Zhou

agents reinforcement-learning multimodal benchmarks

arXiv AI

Jun 9

Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?

arXiv:2605. 28860v2 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities.

By Jeanmely Rojas Nunez, Viraj Sawant, Nathan Allen, Nomgondalai Amgalanbaatar, Yannis Zongo, Vasu Sharma, Maheep Chaudhary

llms reinforcement-learning fine-tuning

One email a morning, machine-written

Related stories

What Does an LLM Learn from Reinforcement Learning? A Mechanistic Interpretability Perspective with Fixed-SAE Track

Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging

SCOPE-RL: Optimizing Reasoning Paths Before and After Success

Boosting Reasoning in Large Multimodal Models via Activation Replay

When RLVR Shrinks the Reasoning Boundary: Diagnosing Pass@k Inversion

Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?