1.2k citations · 1.3k across the 35 of their papers we have counts for
37 papers
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…
DeepTool: Scaling Interleaved Deliberation in Tool-Integrated Reasoning via Process-Supervised Reinforcement Learning
Yang He, Xiao Ding, Bibo Cai +5
Tool-Integrated Reasoning (TIR) extends LLM capabilities by leveraging external environments. However, existing methods lack the deliberation during sequential tool invocation requ…
LSR-Ben: A Logical and Scientific Reasoning Benchmark for Evaluating Process Reward Models
Zhouhao Sun, Xuan Zhang, Xiao Ding +10
Currently, process reward models (PRMs) have exhibited remarkable potential for test-time scaling. Since large language models (LLMs) regularly generate flawed intermediate reasoni…
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…
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…
Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration
Yang Zhao, Yangou Ouyang, Xiao Ding +8
While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effective mechanisms for data allocation…
MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization
Yang Zhao, Hepeng Wang, Xiao Ding +8
Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is primarily confined to domains with ver…
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…
Com: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models
Kai Xiong, Xiao Ding, Yixin Cao +7
Large language models (LLMs) have mastered abundant simple and explicit commonsense knowledge through pre-training, enabling them to achieve human-like performance in simple common…
CrossICL: Cross-Task In-Context Learning via Unsupervised Demonstration Transfer
Jinglong Gao, Xiao Ding, Lingxiao Zou +2
In-Context Learning (ICL) enhances the performance of large language models (LLMs) with demonstrations. However, obtaining these demonstrations primarily relies on manual effort. I…
ExpeTrans: LLMs Are Experiential Transfer Learners
Jinglong Gao, Xiao Ding, Lingxiao Zou +3
Recent studies provide large language models (LLMs) with textual task-solving experiences via prompts to improve their performance. However, previous methods rely on substantial hu…
Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning
Yang He, Xiao Ding, Bibo Cai +5
While reasoning-augmented large language models (RLLMs) significantly enhance complex task performance through extended reasoning chains, they inevitably introduce substantial unne…
Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Models
Zhouhao Sun, Xiao Ding, Li Du +5
Despite significant progress, recent studies indicate that current large language models (LLMs) may still capture dataset biases and utilize them during inference, leading to the p…
UFO-RL: Uncertainty-Focused Optimization for Efficient Reinforcement Learning Data Selection
Yang Zhao, Kai Xiong, Xiao Ding +9
Scaling RL for LLMs is computationally expensive, largely due to multi-sampling for policy optimization and evaluation, making efficient data selection crucial. Inspired by the Zon…
Large Language Models Are Still Misled by Simple Bias Ensembles
Zhouhao Sun, Zhiyuan Kan, Xiao Ding +5
With the evolution of large language models (LLMs), their robustness against individual simple biases has been enhanced. However, we observe that the ensemble of multiple simple bi…
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection
Yang Zhao, Li Du, Xiao Ding +10
Large language models (LLMs) have shown great potential across various industries due to their remarkable ability to generalize through instruction tuning. However, the limited ava…
Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns
Yang Zhao, Li Du, Xiao Ding +3
LLMs' performance on complex tasks is still unsatisfactory. A key issue is that presently LLMs learn in a data-driven schema, while the instructions about these complex tasks are b…
Causal-Guided Active Learning for Debiasing Large Language Models
Li Du, Zhouhao Sun, Xiao Ding +5
Although achieving promising performance, recent analyses show that current generative large language models (LLMs) may still capture dataset biases and utilize them for generation…
Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning
Kai Xiong, Xiao Ding, Li Du +4
Large Language Models (LLMs) are versatile and demonstrate impressive generalization ability by mining and learning information from extensive unlabeled text. However, they still e…
Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion
Jinglong Gao, Chen Lu, Xiao Ding +3
Event Causality Extraction (ECE) aims at extracting causal event pairs from texts. Despite ChatGPT's recent success, fine-tuning small models remains the best approach for the ECE…
Self-Evolving GPT: A Lifelong Autonomous Experiential Learner
Jinglong Gao, Xiao Ding, Yiming Cui +4
To improve the performance of large language models (LLMs), researchers have explored providing LLMs with textual task-solving experience via prompts. However, they rely on manual…
Beyond Fixed Length: Bucket Pre-training is All You Need
Qing Yang, Qiyao Peng, Hongtao Liu +3
Large Language Models (LLMs) have demonstrated exceptional performance across various tasks, with pre-training stage serving as the cornerstone of their capabilities. However, the…
Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation
Zhouhao Sun, Xiao Ding, Li Du +4
Large language models (LLMs) have achieved significant performance in various natural language reasoning tasks. However, they still struggle with performing first-order logic reaso…
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…