2 citations · 2 across the 9 of their papers we have counts for
9 papers
AutoTrainess: Teaching Language Models to Improve Language Models Autonomously
Zhaojian Yu, Penghao Yin, Shuzheng Gao +3
Training language models (LMs) remains a highly human-intensive process, even as frontier language model agents become increasingly capable at software engineering and other long-h…
Dockerless: Environment-Free Program Verifier for Coding Agents
Wenhao Zeng, Yuling Shi, Xiaodong Gu +10
Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning…
SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents
Shuzheng Gao, Wenhao Zeng, Zhaojian Yu +5
Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory management methods typically re…
VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding
Yinghao Wu, Zhuoyan Luo, Yiyao Yu +3
Despite the remarkable progress achieved by recent efficient methods in accelerating multimodal understanding, they still suffer from noticeable performance degradation. Their emph…
AlphaResearch: Accelerating New Algorithm Discovery with Language Models
Zhaojian Yu, Kaiyue Feng, Yilun Zhao +3
LLMs have made significant progress in complex but easy-to-verify problems, yet they still struggle with discovering the unknown. In this paper, we present \textbf{AlphaResearch},…
Z1: Efficient Test-time Scaling with Code
Zhaojian Yu, Yinghao Wu, Yilun Zhao +2
Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, yet this often entails longer contexts and numerous reasoning token c…
HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation
Zhaojian Yu, Yilun Zhao, Arman Cohan +1
We introduce self-invoking code generation, a new task designed to evaluate the progressive reasoning and problem-solving capabilities of LLMs. In this task, models are presented w…
MIGA: Mixture-of-Experts with Group Aggregation for Stock Market Prediction
Zhaojian Yu, Yinghao Wu, Genesis Wang +1
Stock market prediction has remained an extremely challenging problem for many decades owing to its inherent high volatility and low information noisy ratio. Existing solutions bas…
OpenCarbonEval: A Unified Carbon Emission Estimation Framework in Large-Scale AI Models
Zhaojian Yu, Yinghao Wu, Zhuotao Deng +2
In recent years, large-scale auto-regressive models have made significant progress in various tasks, such as text or video generation. However, the environmental impact of these mo…