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Collections including paper arxiv:2503.24235

Reasoning, Thinking, RL and Test-Time Scaling

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Paper • 2412.18319 • Published Dec 24, 2024 • 40
Token-Budget-Aware LLM Reasoning

Paper • 2412.18547 • Published Dec 24, 2024 • 47
Efficiently Serving LLM Reasoning Programs with Certaindex

Paper • 2412.20993 • Published Dec 30, 2024 • 38
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

Paper • 2412.17256 • Published Dec 23, 2024 • 48

Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Paper • 2504.13837 • Published Apr 18 • 134
TTRL: Test-Time Reinforcement Learning

Paper • 2504.16084 • Published Apr 22 • 120
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning

Paper • 2506.01939 • Published Jun 2 • 180

What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55

What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55

Natural Language Reinforcement Learning

Paper • 2411.14251 • Published Nov 21, 2024 • 31
Towards General-Purpose Model-Free Reinforcement Learning

Paper • 2501.16142 • Published Jan 27 • 31
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't

Paper • 2503.16219 • Published Mar 20 • 52
Teaching Large Language Models to Reason with Reinforcement Learning

Paper • 2403.04642 • Published Mar 7, 2024 • 51

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Paper • 2504.01990 • Published Mar 31 • 302
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Paper • 2504.10479 • Published Apr 14 • 282
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55
Seedream 3.0 Technical Report

Paper • 2504.11346 • Published Apr 15 • 68

Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models

Paper • 2504.07951 • Published Apr 10 • 29
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability

Paper • 2504.08003 • Published Apr 9 • 49
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Paper • 2504.11468 • Published Apr 10 • 29
Towards Learning to Complete Anything in Lidar

Paper • 2504.12264 • Published Apr 16 • 10

interesting work

MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization

Paper • 2504.00999 • Published Apr 1 • 95
Any2Caption:Interpreting Any Condition to Caption for Controllable Video Generation

Paper • 2503.24379 • Published Mar 31 • 77
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1

Paper • 2503.24376 • Published Mar 31 • 39
A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond

Paper • 2503.21614 • Published Mar 27 • 42

Test-Time Scaling (TTS)

What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55

RL+reason model

RL + Transformer = A General-Purpose Problem Solver

Paper • 2501.14176 • Published Jan 24 • 28
Towards General-Purpose Model-Free Reinforcement Learning

Paper • 2501.16142 • Published Jan 27 • 31
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Paper • 2501.17161 • Published Jan 28 • 123
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization

Paper • 2412.12098 • Published Dec 16, 2024 • 5

Reasoning, Thinking, RL and Test-Time Scaling

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Paper • 2412.18319 • Published Dec 24, 2024 • 40
Token-Budget-Aware LLM Reasoning

Paper • 2412.18547 • Published Dec 24, 2024 • 47
Efficiently Serving LLM Reasoning Programs with Certaindex

Paper • 2412.20993 • Published Dec 30, 2024 • 38
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

Paper • 2412.17256 • Published Dec 23, 2024 • 48

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Paper • 2504.01990 • Published Mar 31 • 302
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Paper • 2504.10479 • Published Apr 14 • 282
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55
Seedream 3.0 Technical Report

Paper • 2504.11346 • Published Apr 15 • 68

Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Paper • 2504.13837 • Published Apr 18 • 134
TTRL: Test-Time Reinforcement Learning

Paper • 2504.16084 • Published Apr 22 • 120
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning

Paper • 2506.01939 • Published Jun 2 • 180

Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models

Paper • 2504.07951 • Published Apr 10 • 29
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability

Paper • 2504.08003 • Published Apr 9 • 49
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Paper • 2504.11468 • Published Apr 10 • 29
Towards Learning to Complete Anything in Lidar

Paper • 2504.12264 • Published Apr 16 • 10

What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55

interesting work

MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization

Paper • 2504.00999 • Published Apr 1 • 95
Any2Caption:Interpreting Any Condition to Caption for Controllable Video Generation

Paper • 2503.24379 • Published Mar 31 • 77
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1

Paper • 2503.24376 • Published Mar 31 • 39
A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond

Paper • 2503.21614 • Published Mar 27 • 42

What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55

Test-Time Scaling (TTS)

What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

Paper • 2503.24235 • Published Mar 31 • 55

Natural Language Reinforcement Learning

Paper • 2411.14251 • Published Nov 21, 2024 • 31
Towards General-Purpose Model-Free Reinforcement Learning

Paper • 2501.16142 • Published Jan 27 • 31
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't

Paper • 2503.16219 • Published Mar 20 • 52
Teaching Large Language Models to Reason with Reinforcement Learning

Paper • 2403.04642 • Published Mar 7, 2024 • 51

RL+reason model

RL + Transformer = A General-Purpose Problem Solver

Paper • 2501.14176 • Published Jan 24 • 28
Towards General-Purpose Model-Free Reinforcement Learning

Paper • 2501.16142 • Published Jan 27 • 31
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Paper • 2501.17161 • Published Jan 28 • 123
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization

Paper • 2412.12098 • Published Dec 16, 2024 • 5

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