Papers134

#GRPO

Reinforcement World Model Learning for LLM-based Agents

Large language models are great at words, but they struggle to predict what will happen after they act in a changing world.

#Reinforcement World Model Learning#world modeling#LLM agents

Multi-Task GRPO: Reliable LLM Reasoning Across Tasks

Intermediate

Shyam Sundhar Ramesh, Xiaotong Ji et al.Feb 5arXiv

Large language models are usually trained to get good at one kind of reasoning, but real life needs them to be good at many things at once.

#Multi-Task Learning#GRPO#Reinforcement Learning Post-Training

ProAct: Agentic Lookahead in Interactive Environments

Intermediate

Yangbin Yu, Mingyu Yang et al.Feb 5arXiv

ProAct teaches AI agents to think ahead accurately without needing expensive search every time they act.

#ProAct#GLAD#MC-Critic

Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR

Intermediate

Fanfan Liu, Youyang Yin et al.Feb 5arXiv

The paper discovers that popular RLVR methods for training language and vision-language models secretly prefer certain answer lengths, which can hurt learning.

#LUSPO#RLVR#GRPO

Reinforced Attention Learning

Intermediate

Bangzheng Li, Jianmo Ni et al.Feb 4arXiv

This paper teaches AI to pay attention better by training its focus, not just its words.

#Reinforced Attention Learning#attention policy#multimodal LLM

Rethinking the Trust Region in LLM Reinforcement Learning

Intermediate

Penghui Qi, Xiangxin Zhou et al.Feb 4arXiv

The paper shows that the popular PPO method for training language models is unfair to rare words and too gentle with very common words, which makes learning slow and unstable.

#Reinforcement Learning#Proximal Policy Optimization#Trust Region

Privileged Information Distillation for Language Models

Intermediate

Emiliano Penaloza, Dheeraj Vattikonda et al.Feb 4arXiv

The paper shows how to train a language model with special extra hints (privileged information) during practice so it can still do well later without any hints.

#Privileged Information#Knowledge Distillation#π-Distill

WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning

Intermediate

Zelai Xu, Zhexuan Xu et al.Feb 4arXiv

WideSeek-R1 teaches a small 4B-parameter language model to act like a well-run team: one leader plans, many helpers work in parallel, and everyone learns together with reinforcement learning.

#width scaling#multi-agent reinforcement learning#orchestration

Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition

Intermediate

Jinlong Ma, Yu Zhang et al.Feb 4arXiv

The paper teaches multimodal large language models (MLLMs) to stop guessing from just text or just images and instead check both together before answering.

#GMNER#Multimodal Large Language Models#Modality Bias

AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent

Intermediate

Yinyi Luo, Yiqiao Jin et al.Feb 3arXiv

AgentArk teaches one language model to think like a whole team of models that debate, so it can solve tough problems quickly without running a long, expensive debate at answer time.

#multi-agent distillation#process reward model#GRPO

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

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