Dingwei Chen

Currently, most reinforcement learning tasks focus on domains like mathematics and programming, where verification is relatively straightforward. However, in subjective tasks such as role-playing, alignment techniques struggle to make progress, primarily because subjective reward modeling using t...

LLM agents have emerged as powerful systems for tackling multi-turn tasks by interleaving internal reasoning and external tool interactions. Agentic Reinforcement Learning has recently drawn significant research attention as a critical post-training paradigm to further refine these capabilities....

Large reasoning models (LRMs) have achieved remarkable success through step-by-step chains of thought, yet they often suffer from excessive redundancy or unfaithful reasoning. Existing methods for shaping LRM behavior typically rely on reinforcement learning or fine-tuning with gold-standard reas...