9 citations · 11 across the 16 of their papers we have counts for
16 papers
SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning
Haonan He, Haodi Lei, Yun Luo +13
On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models, but applying it to long-context reasoning teachers and short-co…
EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
Zhilin Wang, Han Song, Runzhe Zhan +13
Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it…
ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics
Shunkai Zhang, Haoran Zhang, Yun Luo +15
Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight. Recent evidence…
-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows
Haoran Zhang, Luxin Xu, Zhilin Wang +11
The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in…
Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
Yafu Li, Runzhe Zhan, Haoran Zhang +25
Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performa…
Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning
Qianjia Cheng, Yuchen Zhang, Zhilin Wang +9
Tool-integrated reasoning (TIR) offers a direct way to extend thinking models beyond the limits of text-only reasoning. Paradoxically, we observe that tool-enabled evaluation can d…
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…
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:…
Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning
Zhilin Wang, Zhe Yang, Yun Luo +8
Enhancing the ability of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) to interpret sheet music is a crucial step toward building AI musicians. However,…
Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberation
Haoran Zhang, Yafu Li, Xuyang Hu +4
Large language models (LLMs) are increasingly applied in diverse real-world scenarios, each governed by bespoke behavioral and safety specifications (spec) custom-tailored by users…
SEE: Continual Fine-tuning with Sequential Ensemble of Experts
Zhilin Wang, Yafu Li, Xiaoye Qu +1
Continual fine-tuning of large language models (LLMs) suffers from catastrophic forgetting. Rehearsal-based methods mitigate this problem by retaining a small set of old data. Neve…
Lost in Literalism: How Supervised Training Shapes Translationese in LLMs
Yafu Li, Ronghao Zhang, Zhilin Wang +5
Large language models (LLMs) have achieved remarkable success in machine translation, demonstrating impressive performance across diverse languages. However, translationese, charac…
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing
Zhilin Wang, Yafu Li, Jianhao Yan +2
Dynamical systems theory provides a framework for analyzing iterative processes and evolution over time. Within such systems, repetitive transformations can lead to stable configur…
From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning
Yafu Li, Zhilin Wang, Tingchen Fu +3
Scaling data and model size has been proven effective for boosting the performance of large language models. In addition to training-time scaling, recent studies have revealed that…
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text
Yafu Li, Zhilin Wang, Leyang Cui +3
AI-generated text detection has attracted increasing attention as powerful language models approach human-level generation. Limited work is devoted to detecting (partially) AI-para…
MAGE: Machine-generated Text Detection in the Wild
Yafu Li, Qintong Li, Leyang Cui +6
Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection to mitigate risks like the spread of fake new…