4 papers · 1 filter
Physics-Guided Policy Optimization with Self-Distillation
Ke Wang, Yuning Wu, Haoran Liu +3
Self-distilled policy optimization (SDPO) has become a popular paradigm for LLM post-training, where a model learns from its own predictions conditioned on privileged information.…
CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models
Yuning Wu, Yingmin Liu, Yang Shu
Large language model (LLM) self-correction -- the ability to detect and fix errors in generated outputs -- remains largely ad hoc, relying on generic prompts such as "please recons…
Hindsight-Anchored Policy Optimization: Learning Through Hindsight with Thompson Sampling-Inspired Adaptive Gating
Yuning Wu, Ke Wang, Haoran Liu +3
Reinforcement Learning with Verifiable Rewards improves reasoning in large language models, yet on-policy learning often suffers from cold-start challenges in sparse-reward setting…
R2-Write: Reflection and Revision for Open-Ended Writing with Deep Reasoning
Wanlong Liu, Bo Zhang, Chenliang Li +4
While deep reasoning with long chain-of-thought has dramatically improved large language models in verifiable domains like mathematics, its effectiveness for open-ended tasks such…