Towards General-Purpose Model-Free Reinforcement Learning
Paper
• 2501.16142 • Published
• 31
Agent & RL
-
-
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Paper • 2503.14476 • Published • 144 -
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
Paper • 2504.13837 • Published • 139 -
Learning to Reason under Off-Policy Guidance
Paper • 2504.14945 • Published • 88 -
ToolRL: Reward is All Tool Learning Needs
Paper • 2504.13958 • Published • 49 -
TTRL: Test-Time Reinforcement Learning
Paper • 2504.16084 • Published • 120 -
Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning
Paper • 2504.16656 • Published • 57 -
Reinforcement Learning for Reasoning in Large Language Models with One Training Example
Paper • 2504.20571 • Published • 98 -
xVerify: Efficient Answer Verifier for Reasoning Model Evaluations
Paper • 2504.10481 • Published • 85 -
Rethinking Reflection in Pre-Training
Paper • 2504.04022 • Published • 80 -
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Paper • 2504.10479 • Published • 306 -
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 303 -
Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
Paper • 2503.24290 • Published • 62 -
Distilling LLM Agent into Small Models with Retrieval and Code Tools
Paper • 2505.17612 • Published • 81 -
ARM: Adaptive Reasoning Model
Paper • 2505.20258 • Published • 45 -
Absolute Zero: Reinforced Self-play Reasoning with Zero Data
Paper • 2505.03335 • Published • 189 -
The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
Paper • 2505.22617 • Published • 131 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?
Paper • 2502.12115 • Published • 46 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Paper • 2502.05171 • Published • 152 -
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Paper • 2501.17161 • Published • 124 -
CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction
Paper • 2502.07316 • Published • 50 -
WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Paper • 2507.15061 • Published • 60 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 261 -
MemOS: A Memory OS for AI System
Paper • 2507.03724 • Published • 159 -
Agentic Reinforced Policy Optimization
Paper • 2507.19849 • Published • 158 -
Deep Researcher with Test-Time Diffusion
Paper • 2507.16075 • Published • 68 -
A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence
Paper • 2507.21046 • Published • 84 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 317 -
Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
Paper • 2508.01191 • Published • 238 -
On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification
Paper • 2508.05629 • Published • 183 -
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
Paper • 2508.04700 • Published • 52 -
Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
Paper • 2508.09736 • Published • 58 -
SSRL: Self-Search Reinforcement Learning
Paper • 2508.10874 • Published • 97 -
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL
Paper • 2508.13167 • Published • 129 -
Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
Paper • 2509.08721 • Published • 662 -
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search
Paper • 2509.25454 • Published • 146 -
Less is More: Recursive Reasoning with Tiny Networks
Paper • 2510.04871 • Published • 509 -
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Paper • 2509.02547 • Published • 230 -
A Survey of Reinforcement Learning for Large Reasoning Models
Paper • 2509.08827 • Published • 190 -
Agent Learning via Early Experience
Paper • 2510.08558 • Published • 273 -
Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense
Paper • 2510.07242 • Published • 30 -
Multi-Agent Tool-Integrated Policy Optimization
Paper • 2510.04678 • Published • 31 -
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Paper • 2510.04618 • Published • 129 -
RLP: Reinforcement as a Pretraining Objective
Paper • 2510.01265 • Published • 44 -
It Takes Two: Your GRPO Is Secretly DPO
Paper • 2510.00977 • Published • 32 -
DCPO: Dynamic Clipping Policy Optimization
Paper • 2509.02333 • Published • 22 -
The Art of Scaling Reinforcement Learning Compute for LLMs
Paper • 2510.13786 • Published • 32 -
What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
Paper • 2511.15593 • Published • 58 -
MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling
Paper • 2511.11793 • Published • 187 -
P1: Mastering Physics Olympiads with Reinforcement Learning
Paper • 2511.13612 • Published • 134 -
LightRAG: Simple and Fast Retrieval-Augmented Generation
Paper • 2410.05779 • Published • 28 -
OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists
Paper • 2511.16931 • Published • 8 -
Budget-Aware Tool-Use Enables Effective Agent Scaling
Paper • 2511.17006 • Published • 32