From the 1 of 7 linked papers with an AI index.
7 papers
-OPSD: Deriving with Policy Optimization, Training with Self-Distillation
Jiawei Xu, Minghui Liu, Juzheng Zhang +2
The paper proposes β‑OPSD, a generalized on‑policy self‑distillation method that treats the KL regularization weight as a tunable parameter, enabling a controlled interpolation bet…
Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models
Jiawei Xu, Minghui Liu, Aakriti Agrawal +2
Masked diffusion language models decode by iteratively unmasking tokens, where the unmasking order defines an "order of thought" that strongly influences generation quality yet is…
VeriGate: Verifier-Gated Step-Level Supervision for GRPO
Aakriti Agrawal, Minghui Liu, Furong Huang
Group Relative Policy Optimization (GRPO) is an effective recipe for training reasoning models with verifier-based outcome rewards, but its supervision is sparse: when all sampled…
SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones?
Sy-Tuyen Ho, Minghui Liu, Huy Nghiem +1
Autonomous AI research agents aim to accelerate scientific discovery by automating the research pipeline, from hypothesis generation to peer review. However, existing benchmarks ra…
Agentic Critical Training
Weize Liu, Minghui Liu, Sy-Tuyen Ho +3
Training large language models (LLMs) as autonomous agents often begins with imitation learning, but it only teaches agents what to do without understanding why: agents never contr…
Hold Onto That Thought: Assessing KV Cache Compression On Reasoning
Minghui Liu, Aadi Palnitkar, Tahseen Rabbani +9
Large language models (LLMs) have demonstrated remarkable performance on long-context tasks, but are often bottlenecked by memory constraints. Namely, the KV cache, which is used t…