Papers159

#GRPO

LSRIF: Logic-Structured Reinforcement Learning for Instruction Following

Real instructions often have logic like and first-then and if-else and this paper teaches models to notice and obey that logic.

#instruction following#logical structures#parallel constraints

Not triaged yet

Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards

Intermediate

Jiajie Zhang, Xin Lv et al.Jan 9arXiv

The paper fixes a big problem in training web-searching AI: rewarding only the final answer makes agents cut corners and sometimes hallucinate.

#deep search agents#reinforcement learning#rubric rewards

Not triaged yet

An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift

Intermediate

Constantinos Karouzos, Xingwei Tan et al.Jan 9arXiv

Preference tuning teaches language models to act the way people like, but those habits can fall apart when the topic or style changes (domain shift).

#preference tuning#domain shift#supervised fine-tuning

Not triaged yet

GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization

Intermediate

Shih-Yang Liu, Xin Dong et al.Jan 8arXiv

When a model learns from many rewards at once, a popular method called GRPO can accidentally squash different reward mixes into the same learning signal, which confuses training.

#GDPO#GRPO#multi-reward reinforcement learning

Not triaged yet

RelayLLM: Efficient Reasoning via Collaborative Decoding

Intermediate

Chengsong Huang, Tong Zheng et al.Jan 8arXiv

RelayLLM lets a small model do the talking and only asks a big model for help on a few, truly hard tokens.

#token-level collaboration#<call>n</call> command#collaborative decoding

Not triaged yet

SmartSearch: Process Reward-Guided Query Refinement for Search Agents

Intermediate

Tongyu Wen, Guanting Dong et al.Jan 8arXiv

SmartSearch teaches search agents to fix their own bad search queries while they are thinking, not just their final answers.

#Search agents#Process rewards#Query refinement

Not triaged yet

AgentOCR: Reimagining Agent History via Optical Self-Compression

Intermediate

Lang Feng, Fuchao Yang et al.Jan 8arXiv

AgentOCR turns an agent’s long text history into pictures so it can remember more using fewer tokens.

#AgentOCR#optical self-compression#visual tokens

Not triaged yet

Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization

Intermediate

Mizanur Rahman, Mohammed Saidul Islam et al.Jan 8arXiv

This paper teaches a model to turn a question about a table into both a short answer and a clear, correct chart.

#Text-to-Visualization#Reinforcement Learning#GRPO

Not triaged yet

Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes

Intermediate

Jing Tan, Zhaoyang Zhang et al.Jan 5arXiv

Talk2Move is a training recipe that lets an image editor move, rotate, and resize the exact object you mention using plain text, while keeping the rest of the picture stable.

#text-guided image editing#object-level transformation#reinforcement learning

Not triaged yet

VAR RL Done Right: Tackling Asynchronous Policy Conflicts in Visual Autoregressive Generation

Intermediate

Shikun Sun, Liao Qu et al.Jan 5arXiv

Visual Autoregressive (VAR) models draw whole grids of image tokens at once across multiple scales, which makes standard reinforcement learning (RL) unstable.

#Visual Autoregressive (VAR)#Reinforcement Learning#GRPO

Not triaged yet

MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics

Intermediate

Zhuofan Shi, Hubao A et al.Jan 5arXiv

MDAgent2 is a special helper built from large language models (LLMs) that can both answer questions about molecular dynamics and write runnable LAMMPS simulation code.

#Molecular Dynamics#LAMMPS#Code Generation

Not triaged yet

The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving

Intermediate

Max Ruiz Luyten, Mihaela van der SchaarJan 2arXiv

Modern AI models can get very good at being correct, but in the process they often lose their ability to think in many different ways.

#Distributional Creative Reasoning#diversity energy#creativity kernel

Not triaged yet

7 8 9 10 11