111 citations · 224 across the 14 of their papers we have counts for
16 papers · 1 filter
The Imitation Game: When LLMs Learn to Reason Like Programs via Code-Centric Reasoning Data Synthesis
Jinyang Zhang, Weibin Liao, Keqin Bao +10
Large Language Models (LLMs) excel at programming tasks but frequently fail at deterministic, fine-grained reasoning in natural language, relying heavily on semantic approximations…
ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning
Xiaoyuan Li, Keqin Bao, Moxin Li +5
Rubric-based rewards offer a promising way to extend reinforcement learning (RL) for large language models beyond tasks with automatically verifiable answers. However, scaling rubr…
Unified Data Selection for LLM Reasoning
Xiaoyuan Li, Yubo Ma, Chengpeng Li +6
Effectively training Large Language Models (LLMs) for complex, long-CoT reasoning is often bottlenecked by the need for massive high-quality reasoning data. Existing methods are ei…
SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs
Xiaoyuan Li, Moxin Li, Keqin Bao +4
Skill libraries enable large language model agents to reuse experience from past interactions, but most existing libraries store skills as isolated entries and retrieve them only b…
SAGE: Scalable Automated Robustness Augmentation for LLM Knowledge Evaluation
Xiaoyuan Li, Yuzhe Wang, Moxin Li +6
Large Language Models (LLMs) achieve strong performance on standard knowledge evaluation benchmarks, yet recent work shows that their knowledge capabilities remain brittle under qu…
On Predicting the Post-training Potential of Pre-trained LLMs
Xiaoyuan Li, Yubo Ma, Kexin Yang +5
The performance of Large Language Models (LLMs) on downstream tasks is fundamentally constrained by the capabilities acquired during pre-training. However, traditional benchmarks l…
Teaching Language Models to Reason with Tools
Chengpeng Li, Zhengyang Tang, Ziniu Li +8
Large reasoning models (LRMs) like OpenAI-o1 have shown impressive capabilities in natural language reasoning. However, these models frequently demonstrate inefficiencies or inaccu…
Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code
Keqin Bao, Nuo Chen, Xiaoyuan Li +5
Enhancing reasoning capabilities remains a central focus in the LLM reasearch community. A promising direction involves requiring models to simulate code execution step-by-step to…
CoRT: Code-integrated Reasoning within Thinking
Chengpeng Li, Zhengyang Tang, Ziniu Li +8
Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable progress in natural language reasoning with long chain-of-thought (CoT), yet they remain inefficient or…
Qwen3 Technical Report
An Yang, Anfeng Li, Baosong Yang +57
In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, a…
MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation
Xiaoyuan Li, Keqin Bao, Yubo Ma +6
Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. However, current evaluations predominantly focus on single-turn reasoning s…
HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
Xiaoyuan Li, Moxin Li, Rui Men +6
Large language models (LLMs) have shown remarkable capabilities in commonsense reasoning; however, some variations in questions can trigger incorrect responses. Do these models tru…
Qwen2.5 Technical Report
Qwen, :, An Yang +41
In this report, we introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has been sign…
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
Keqin Bao, Yu Wan, Dayiheng Liu +5
Fine-grained information on translation errors is helpful for the translation evaluation community. Existing approaches can not synchronously consider error position and type, fail…
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
Keqin Bao, Yu Wan, Dayiheng Liu +5
In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation). Specifically, our syst…
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task
Yu Wan, Keqin Bao, Dayiheng Liu +5
In this report, we present our submission to the WMT 2022 Metrics Shared Task. We build our system based on the core idea of UNITE (Unified Translation Evaluation), which unifies s…