1 citations · 1 across the 9 of their papers we have counts for
10 papers
Towards Scalable RLVR: Multimodal Instruction Following Data Synthesis and Distillation
Yirong Zeng, Zhang Sai, Yuxian Wang +4
Multimodal instruction following (MMIF) is crucial for building generalist agents. However, current training paradigms rely heavily on Supervised Fine-Tuning (SFT), which often lea…
EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent
Yirong Zeng, Shen You, Jinhang Feng +8
The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across stateful workspaces. While Agentic R…
TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles
Yirong Zeng, Yufei Liu, Xiao Ding +9
Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output length) to unverifiable ones…
AutoTool: Automatic Scaling of Tool-Use Capabilities in RL via Decoupled Entropy Constraints
Yirong Zeng, Xiao Ding, Yufei Liu +9
Tool use represents a critical capability for AI agents, with recent advances focusing on leveraging reinforcement learning (RL) to scale up the explicit reasoning process to achie…
The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?
Yirong Zeng, Shen You, Yufei Liu +9
Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon: tool overuse, the unnecess…
Precision over Diversity: High-Precision Reward Generalizes to Robust Instruction Following
Yirong Zeng, Yufei Liu, Xiao Ding +9
A central belief in scaling reinforcement learning with verifiable rewards for instruction following (IF) tasks is that, a diverse mixture of verifiable hard and unverifiable soft…
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch
Yirong Zeng, Xiao Ding, Yutai Hou +9
Training tool-augmented LLMs has emerged as a promising approach to enhancing language models' capabilities for complex tasks. The current supervised fine-tuning paradigm relies on…
ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution
Xu Huang, Weiwen Liu, Xingshan Zeng +8
The tool-using capability of large language models (LLMs) enables them to access up-to-date external information and handle complex tasks. Current approaches to enhancing this capa…
iTool: Reinforced Fine-Tuning with Dynamic Deficiency Calibration for Advanced Tool Use
Yirong Zeng, Xiao Ding, Yuxian Wang +8
Augmenting large language models (LLMs) with external tools is a promising approach to enhance their capabilities, especially for complex tasks. Synthesizing tool-use data through…
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict
Yirong Zeng, Xiao Ding, Yi Zhao +5
Fact-checking is the task of verifying the factuality of a given claim by examining the available evidence. High-quality evidence plays a vital role in enhancing fact-checking syst…