15 papers · 1 filter
Characterizing, Evaluating, and Optimizing Complex Reasoning
Haoran Zhang, Yafu Li, Zhi Wang +4
Large Reasoning Models (LRMs) increasingly rely on reasoning traces with complex internal structures. However, existing work lacks a unified answer to three fundamental questions:…
Draft-OPD: On-Policy Distillation for Speculative Draft Models
Haodi Lei, Yafu Li, Haoran Zhang +8
Speculative decoding accelerates large language model inference by pairing a target model with a lightweight draft model whose proposed tokens are verified in parallel. A common wa…
LatentMem: Customizing Latent Memory for Multi-Agent Systems
Muxin Fu, Xiangyuan Xue, Yafu Li +5
Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, wherein multi-agent memory serves as a pivotal mechanism for continual…
Think Longer to Explore Deeper: Learn to Explore In-Context via Length-Incentivized Reinforcement Learning
Futing Wang, Jianhao Yan, Yun Luo +6
Achieving effective test-time scaling requires models to engage in In-Context Exploration -- the intrinsic ability to generate, verify, and refine multiple reasoning hypotheses wit…
FaithRL: Learning to Reason Faithfully through Step-Level Faithfulness Maximization
Runquan Gui, Yafu Li, Xiaoye Qu +3
Reinforcement Learning with Verifiable Rewards (RLVR) has markedly improved the performance of Large Language Models (LLMs) on tasks requiring multi-step reasoning. However, most R…
New Skills or Sharper Primitives? A Probabilistic Perspective on the Emergence of Reasoning in RLVR
Zhilin Wang, Yafu Li, Shunkai Zhang +4
Whether Reinforcement Learning with Verifiable Rewards (RLVR) endows Large Language Models (LLMs) with new capabilities or merely elicits latent traces remains a central debate. In…