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All SourcesarXiv
#reinforcement learning

V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval

Intermediate
Dongyang Chen, Chaoyang Wang et al.Feb 5arXiv

V-Retrver is a new way for AI to search across text and images by double-checking tiny visual details instead of only guessing from words.

#V-Retrver#multimodal retrieval#agentic reasoning

ERNIE 5.0 Technical Report

Intermediate
Haifeng Wang, Hua Wu et al.Feb 4arXiv

ERNIE 5.0 is a single giant model that can read and create text, images, video, and audio by predicting the next pieces step by step, like writing a story one line at a time.

#ERNIE 5.0#unified autoregressive model#mixture-of-experts

Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission via Agentic Reinforcement Learning

Intermediate
Yansong Ning, Jun Fang et al.Feb 4arXiv

Agent-Omit teaches AI agents to skip unneeded thinking and old observations, cutting tokens while keeping accuracy high.

#LLM agents#reinforcement learning#agentic RL

Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration

Intermediate
Bowei He, Minda Hu et al.Feb 3arXiv

This paper teaches AI to look things up on the web and fix its own mistakes mid-thought instead of starting over from scratch.

#search-integrated reasoning#reinforcement learning#credit assignment

Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation

Intermediate
Changze Lv, Jie Zhou et al.Feb 3arXiv

DeepResearch agents write long, evidence-based reports, but teaching and grading them is hard because there is no single 'right answer' to score against.

#DeepResearch#query-specific rubrics#human preference learning

SWE-World: Building Software Engineering Agents in Docker-Free Environments

Intermediate
Shuang Sun, Huatong Song et al.Feb 3arXiv

SWE-World lets code-fixing AI agents practice and learn without heavy Docker containers by using smart models that pretend to be the computer and tests.

#SWE-World#software engineering agents#Docker-free training

SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training

Intermediate
Huatong Song, Lisheng Huang et al.Feb 3arXiv

SWE-Master is a fully open, step-by-step recipe for turning a regular coding model into a strong software-fixing agent that works across many steps, files, and tests.

#SWE-Master#software engineering agent#long-horizon SFT

Self-Hinting Language Models Enhance Reinforcement Learning

Intermediate
Baohao Liao, Hanze Dong et al.Feb 3arXiv

When rewards are rare, a popular training method for language models (GRPO) often stops learning because every try in a group gets the same score, so there is nothing to compare.

#reinforcement learning#GRPO#self-hinting

Neural Predictor-Corrector: Solving Homotopy Problems with Reinforcement Learning

Intermediate
Jiayao Mai, Bangyan Liao et al.Feb 3arXiv

This paper shows that many hard math and AI problems can be solved with one shared idea called homotopy, where we move from an easy version of a problem to the real one step by step.

#homotopy continuation#predictor-corrector#reinforcement learning

RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System

Beginner
Yinjie Wang, Tianbao Xie et al.Feb 2arXiv

RLAnything is a new reinforcement learning (RL) framework that trains three things together at once: the policy (the agent), the reward model (the judge), and the environment (the tasks).

#reinforcement learning#closed-loop optimization#reward modeling

Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability

Intermediate
Xiao Liang, Zhong-Zhi Li et al.Feb 2arXiv

The paper trains language models to solve hard problems by first breaking them into smaller parts and then solving those parts, instead of only thinking in one long chain.

#divide-and-conquer reasoning#chain-of-thought#reinforcement learning

MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents

Intermediate
Haozhen Zhang, Quanyu Long et al.Feb 2arXiv

MemSkill turns memory operations for AI agents into learnable skills instead of fixed, hand-made rules.

#memory skills#LLM agents#skill bank
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