Papers127

#reinforcement learning

Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors

Zhiwei Zhang, Fei Zhao et al.Jan 22arXiv

Small AI models often stumble when a tool call fails and then get stuck repeating bad calls instead of fixing the mistake.

#FISSION-GRPO#error recovery#tool use

PROGRESSLM: Towards Progress Reasoning in Vision-Language Models

Intermediate

Jianshu Zhang, Chengxuan Qian et al.Jan 21arXiv

This paper asks a new question for vision-language models: not just 'What do you see?' but 'How far along is the task right now?'

#progress reasoning#vision-language models#episodic retrieval

The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models

Beginner

Zanlin Ni, Shenzhi Wang et al.Jan 21arXiv

Diffusion language models can write tokens in any order, but that freedom can accidentally hurt their ability to reason well.

#diffusion language model#arbitrary order generation#autoregressive training

FARE: Fast-Slow Agentic Robotic Exploration

Beginner

Shuhao Liao, Xuxin Lv et al.Jan 21arXiv

Robots used to explore by following simple rules or short-term rewards, which often made them waste time and backtrack a lot.

#autonomous exploration#fast-slow thinking#hierarchical planning

KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning

Intermediate

Egor Cherepanov, Daniil Zelezetsky et al.Jan 20arXiv

KAGE-Bench is a fast, carefully controlled benchmark that tests how well reinforcement learning (RL) agents trained on pixels handle specific visual changes, like new backgrounds or lighting, without changing the actual game rules.

#reinforcement learning#visual generalization#KAGE-Env

InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning

Intermediate

Matthew Y. R. Yang, Hao Bai et al.Jan 20arXiv

The paper introduces Intervention Training (InT), a simple way for a language model to find and fix the first wrong step in its own reasoning using a short, targeted correction.

#Intervention Training#credit assignment#LLM reasoning

Toward Efficient Agents: Memory, Tool learning, and Planning

Intermediate

Xiaofang Yang, Lijun Li et al.Jan 20arXiv

This survey explains how to make AI agents not just smart, but also efficient with their time, memory, and tool use.

#agent efficiency#memory compression#tool learning

Behavior Knowledge Merge in Reinforced Agentic Models

Intermediate

Xiangchi Yuan, Dachuan Shi et al.Jan 20arXiv

The paper solves a big problem: when you merge several reinforcement-learned models, their special skills get watered down by simple averaging.

#reinforcement learning#model merging#task vectors

Think3D: Thinking with Space for Spatial Reasoning

Beginner

Zaibin Zhang, Yuhan Wu et al.Jan 19arXiv

Think3D lets AI models stop guessing from flat pictures and start exploring real 3D space, like walking around a room in a video game.

#Think3D#spatial reasoning#3D reconstruction

PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models

Intermediate

Qiyuan Zhang, Biao Gong et al.Jan 16arXiv

This paper teaches video-making AIs to follow real-world physics, so rolling balls roll right and collisions look believable.

#physics-aware video generation#rigid body motion#reinforcement learning

BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search

Intermediate

Shiyu Liu, Yongjing Yin et al.Jan 16arXiv

RL-trained search agents often sound confident even when they don’t know, which can mislead people.

#agentic search#reinforcement learning#boundary awareness

Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey

Beginner

Caihua Li, Lianghong Guo et al.Jan 15arXiv

This paper is the first big map of how AI can fix real software problems, not just write short code snippets.

#SWE-bench#issue resolution#AI coding agents

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