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Efficient RLVR Training via Weighted Mutual Information Data Selection

Intermediate

Xinyu Zhou, Boyu Zhu et al.Mar 2arXiv

Reinforcement learning (RL) trains language models by letting them try answers and learn from rewards, but training is slow if we pick the wrong practice questions.

#Reinforcement Learning#RLVR#Data Selection

SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving

Intermediate

Chaofan Tao, Jierun Chen et al.Jan 4arXiv

SWE-Lego shows that a simple training method called supervised fine-tuning (SFT), when done carefully, can teach AI to fix real software bugs very well.

#SWE-Lego#Supervised Fine-Tuning#Error Masking

MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning

Intermediate

Jiawei Chen, Xintian Shen et al.Dec 29arXiv

MindWatcher is a smart AI agent that can think step by step and decide when to use tools like web search, image zooming, and a code calculator to solve tough, multi-step problems.

#Tool-Integrated Reasoning#Interleaved Thinking#Multimodal Chain-of-Thought

Papers3

Efficient RLVR Training via Weighted Mutual Information Data Selection

SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving

MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning