9 papers
Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation
Yuxuan Jiang, Runchao Li, Shubhashis Roy Dipta +2
While recent work in Reinforcement Learning with Verifiable Rewards (RLVR) has shown that a small subset of critical tokens disproportionately drives reasoning gains, an analogous…
Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance
Yuxuan Jiang, Francis Ferraro
On-Policy Distillation (OPD) improves large language model reasoning by training a student model on trajectories sampled from its own policy under teacher supervision. Although OPD…
SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models
Yuxuan Jiang, Francis Ferraro
Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning. While process-level reward mode…
Learning How to Use Tools, Not Just When: Pattern-Aware Tool-Integrated Reasoning
Ningning Xu, Yuxuan Jiang, Shubhashis Roy Dipta +1
Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems. Prior work has mainly studied when to invoke tools, while…
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding
Yandi Wang, Libin Zhan, Ziwei Huang +5
Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multimodal Large Language Models…
DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models
Yuxuan Jiang, Dawei Li, Francis Ferraro
While Large Reasoning Models (LRMs) have demonstrated success in complex reasoning tasks through long chain-of-thought (CoT) reasoning, their inference often involves excessively v…