From the 1 of 15 linked papers with an AI index.
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Matching Supervision to the Student's Learning Capacity: A Unified Framework for On-Policy Self-Distillation
Yongkang Yang, Zhezheng Hao, Hong Zhang +8
On-policy self-distillation (OPSD) improves the reasoning abilities of LLMs by internalizing privileged context into model parameters through self-distillation. Two recent research…
Echo: Learning from Experience Data via User-Driven Refinement
Hande Dong, Xiaoyun Liang, Jiarui Yu +15
Static "human data" faces inherent limitations: it is expensive to scale and bounded by the knowledge of its creators. Continuous learning from "experience data" - interactions bet…
Implicit Safety Alignment from Crowd Preferences
Qian Lin, Daniel S. Brown
Reinforcement Learning from Human Feedback (RLHF) can reveal implicit objectives such as safety considerations that go beyond task completion. In this work, we focus on the common…
ReCreate: Reasoning and Creating Domain Agents Driven by Experience
Zhezheng Hao, Hong Wang, Jian Luo +6
Large Language Model agents are reshaping the industrial landscape. However, most practical agents remain human-designed because tasks differ widely, making them labor-intensive to…
Scheduling Your LLM Reinforcement Learning with Reasoning Trees
Hong Wang, Zhezheng Hao, Jian Luo +6
Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing a query's `Reasoning Tree'. This…
Unleashing the True Potential of LLMs: A Feedback-Triggered Self-Correction with Long-Term Multipath Decoding
Jipeng Li, Zeyu Gao, Yubin Qi +3
Large Language Models (LLMs) have achieved remarkable performance across diverse tasks, yet their susceptibility to generating incorrect content during inference remains a critical…