10 citations · 23 across the 17 of their papers we have counts for
11 papers · 1 filter
Toward Autonomous Long-Horizon Engineering for ML Research
Guoxin Chen, Jie Chen, Lei Chen +7
Agentic systems increasingly automate pieces of AI research. Yet turning underspecified research objectives into runnable, experimentally validated ML systems remains a central bot…
BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?
Guoxin Chen, Fanzhe Meng, Jiale Zhao +12
Current code-agent benchmarks primarily evaluate localized issue resolution within a single target repository, leaving under-tested many software engineering tasks that require ext…
From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR
Jia Deng, Jie Chen, Zhipeng Chen +7
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs). Unlike traditiona…
Decomposing the Entropy-Performance Exchange: The Missing Keys to Unlocking Effective Reinforcement Learning
Jia Deng, Jie Chen, Zhipeng Chen +2
Recently, reinforcement learning with verifiable rewards (RLVR) has been widely used for enhancing the reasoning abilities of large language models (LLMs). A core challenge in RLVR…
An Empirical Study on Eliciting and Improving R1-like Reasoning Models
Zhipeng Chen, Yingqian Min, Beichen Zhang +10
In this report, we present the third technical report on the development of slow-thinking models as part of the STILL project. As the technical pathway becomes clearer, scaling RL…
Enhancing LLM Reasoning with Reward-guided Tree Search
Jinhao Jiang, Zhipeng Chen, Yingqian Min +12
Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By alloc…
YuLan-Mini: An Open Data-efficient Language Model
Yiwen Hu, Huatong Song, Jia Deng +8
Effective pre-training of large language models (LLMs) has been challenging due to the immense resource demands and the complexity of the technical processes involved. This paper p…
Towards Effective and Efficient Continual Pre-training of Large Language Models
Jie Chen, Zhipeng Chen, Jiapeng Wang +16
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents…
LLMBox: A Comprehensive Library for Large Language Models
Tianyi Tang, Yiwen Hu, Bingqian Li +22
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. T…
Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
Jie Chen, Yupeng Zhang, Bingning Wang +3
Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs). Studies have shown that synthetic…
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models
Junyi Li, Jie Chen, Ruiyang Ren +4
In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great challenge to trustworthy and reliable deployment…