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On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
Arxiv
0+阅读 · 3月17日
Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models
Arxiv
0+阅读 · 3月14日
GRPO and Reflection Reward for Mathematical Reasoning in Large Language Models
Arxiv
0+阅读 · 3月14日
TikZilla: Scaling Text-to-TikZ with High-Quality Data and Reinforcement Learning
Arxiv
0+阅读 · 3月3日
Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
Arxiv
0+阅读 · 3月11日
TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training
Arxiv
0+阅读 · 3月2日
KLong: Training LLM Agent for Extremely Long-horizon Tasks
KLong: Training LLM Agent for Extremely Long-horizon Tasks
Arxiv
0+阅读 · 2月19日
Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale
Arxiv
0+阅读 · 2月17日
Towards On-Policy SFT: Distribution Discriminant Theory and its Applications in LLM Training
Arxiv
0+阅读 · 2月12日
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
Arxiv
0+阅读 · 2月10日
Data Repetition Beats Data Scaling in Long-CoT Supervised Fine-Tuning
Arxiv
0+阅读 · 2月11日
Supervised Fine-Tuning Needs to Unlock the Potential of Token Priority
Arxiv
0+阅读 · 2月9日
SED-SFT: Selectively Encouraging Diversity in Supervised Fine-Tuning
Arxiv
0+阅读 · 2月7日
Beyond Imitation: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models
Arxiv
0+阅读 · 2月16日
Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization
Arxiv
0+阅读 · 2月16日
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