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Collections including paper arxiv:2509.25454

Towards General-Purpose Model-Free Reinforcement Learning

Paper • 2501.16142 • Published Jan 27 • 30
DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Paper • 2503.14476 • Published Mar 18 • 141
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Paper • 2504.13837 • Published Apr 18 • 135
Learning to Reason under Off-Policy Guidance

Paper • 2504.14945 • Published Apr 21 • 88

Less is More: Recursive Reasoning with Tiny Networks

Paper • 2510.04871 • Published 20 days ago • 446
Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play

Paper • 2509.25541 • Published 27 days ago • 136
Agent Learning via Early Experience

Paper • 2510.08558 • Published 17 days ago • 241
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133

What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT

Paper • 2509.19284 • Published Sep 23 • 22
Soft Tokens, Hard Truths

Paper • 2509.19170 • Published Sep 23 • 15
CompLLM: Compression for Long Context Q&A

Paper • 2509.19228 • Published Sep 23 • 7
Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yet

Paper • 2509.06861 • Published Sep 8 • 8

Large Language Models

Universal Deep Research: Bring Your Own Model and Strategy

Paper • 2509.00244 • Published Aug 29 • 13
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Paper • 2509.02547 • Published Sep 2 • 218
Efficient Multi-modal Large Language Models via Progressive Consistency Distillation

Paper • 2510.00515 • Published 26 days ago • 39
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review

Paper • 2504.19678 • Published Apr 28 • 3
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133
Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation

Paper • 2509.25849 • Published 27 days ago • 46

Reinforcement learning

Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning

Paper • 2407.20798 • Published Jul 30, 2024 • 24
Offline Reinforcement Learning for LLM Multi-Step Reasoning

Paper • 2412.16145 • Published Dec 20, 2024 • 38
REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models

Paper • 2501.03262 • Published Jan 4 • 102
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Paper • 2502.18449 • Published Feb 25 • 75

DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133

Open Data Synthesis For Deep Research

Paper • 2509.00375 • Published Aug 30 • 68
Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training

Paper • 2509.03403 • Published Sep 3 • 21
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations

Paper • 2509.03405 • Published Sep 3 • 23
SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs

Paper • 2509.00930 • Published Aug 31 • 4

Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

Paper • 2508.09834 • Published Aug 13 • 53
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Paper • 2509.02547 • Published Sep 2 • 218
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133
DeMo: Decoupled Momentum Optimization

Paper • 2411.19870 • Published Nov 29, 2024 • 6

about 11 hours ago

lusxvr/nanoVLM-222M

Image-Text-to-Text • 0.2B • Updated May 8 • 249 • 96
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Paper • 2503.09516 • Published Mar 12 • 36
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Paper • 2505.24863 • Published May 30 • 97
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning

Paper • 2505.17667 • Published May 23 • 88

Towards General-Purpose Model-Free Reinforcement Learning

Paper • 2501.16142 • Published Jan 27 • 30
DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Paper • 2503.14476 • Published Mar 18 • 141
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Paper • 2504.13837 • Published Apr 18 • 135
Learning to Reason under Off-Policy Guidance

Paper • 2504.14945 • Published Apr 21 • 88

Reinforcement learning

Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning

Paper • 2407.20798 • Published Jul 30, 2024 • 24
Offline Reinforcement Learning for LLM Multi-Step Reasoning

Paper • 2412.16145 • Published Dec 20, 2024 • 38
REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models

Paper • 2501.03262 • Published Jan 4 • 102
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Paper • 2502.18449 • Published Feb 25 • 75

Less is More: Recursive Reasoning with Tiny Networks

Paper • 2510.04871 • Published 20 days ago • 446
Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play

Paper • 2509.25541 • Published 27 days ago • 136
Agent Learning via Early Experience

Paper • 2510.08558 • Published 17 days ago • 241
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133

DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133

What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT

Paper • 2509.19284 • Published Sep 23 • 22
Soft Tokens, Hard Truths

Paper • 2509.19170 • Published Sep 23 • 15
CompLLM: Compression for Long Context Q&A

Paper • 2509.19228 • Published Sep 23 • 7
Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yet

Paper • 2509.06861 • Published Sep 8 • 8

Open Data Synthesis For Deep Research

Paper • 2509.00375 • Published Aug 30 • 68
Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training

Paper • 2509.03403 • Published Sep 3 • 21
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations

Paper • 2509.03405 • Published Sep 3 • 23
SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs

Paper • 2509.00930 • Published Aug 31 • 4

Large Language Models

Universal Deep Research: Bring Your Own Model and Strategy

Paper • 2509.00244 • Published Aug 29 • 13
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Paper • 2509.02547 • Published Sep 2 • 218
Efficient Multi-modal Large Language Models via Progressive Consistency Distillation

Paper • 2510.00515 • Published 26 days ago • 39
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133

Speed Always Wins: A Survey on Efficient Architectures for Large Language Models

Paper • 2508.09834 • Published Aug 13 • 53
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Paper • 2509.02547 • Published Sep 2 • 218
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133
DeMo: Decoupled Momentum Optimization

Paper • 2411.19870 • Published Nov 29, 2024 • 6

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review

Paper • 2504.19678 • Published Apr 28 • 3
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Paper • 2509.25454 • Published 27 days ago • 133
Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation

Paper • 2509.25849 • Published 27 days ago • 46

about 11 hours ago

lusxvr/nanoVLM-222M

Image-Text-to-Text • 0.2B • Updated May 8 • 249 • 96
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Paper • 2503.09516 • Published Mar 12 • 36
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Paper • 2505.24863 • Published May 30 • 97
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning

Paper • 2505.17667 • Published May 23 • 88

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