4 citations · 4 across the 12 of their papers we have counts for
12 papers
Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime
Tianshu Zhu, Wenyu Zhang, Xiaoying Zuo +8
Agentic reinforcement learning (RL) for software engineering spends much of its compute on stateful trajectories whose grouped binary rewards are highly skewed and weakly contrasti…
AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning
Haotian Zhao, Songlin Zhou, Yuxin Zhang +9
Reinforcement learning (RL) has substantially improved the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks. However, effective…
MEMENTO: Teaching LLMs to Manage Their Own Context
Vasilis Kontonis, Yuchen Zeng, Shivam Garg +7
Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state. We introduce MEMENTO: a method that teaches model…
Demonstrations, CoT, and Prompting: A Theoretical Analysis of ICL
Xuhan Tong, Yuchen Zeng, Jiawei Zhang
In-Context Learning (ICL) enables pretrained LLMs to adapt to downstream tasks by conditioning on a small set of input-output demonstrations, without any parameter updates. Althoug…
SWE-Hub: A Unified Production System for Scalable, Executable Software Engineering Tasks
Yucheng Zeng, Shupeng Li, Daxiang Dong +11
Progress in software-engineering agents is increasingly constrained by the scarcity of executable, scalable, and realistic data for training and evaluation. This scarcity stems fro…
LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks
Yucheng Zeng, Weipeng Lu, Linyun Liu +9
The evolution of Large Language Models (LLMs) from static instruction-followers to autonomous agents necessitates operating within complex, stateful environments to achieve precise…
QianfanHuijin Technical Report: A Novel Multi-Stage Training Paradigm for Finance Industrial LLMs
Shupeng Li, Weipeng Lu, Linyun Liu +16
Domain-specific enhancement of Large Language Models (LLMs) within the financial context has long been a focal point of industrial application. While previous models such as Bloomb…
ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning
Yuchen Zeng, Shuibai Zhang, Wonjun Kang +9
Large Reasoning Models (LRMs) are Large Language Models (LLMs) explicitly trained to generate long-form Chain-of-Thoughts (CoTs), achieving impressive success on challenging tasks…
ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs
Wonjun Kang, Kevin Galim, Seunghyuk Oh +8
While most autoregressive LLMs are constrained to one-by-one decoding, diffusion LLMs (dLLMs) have attracted growing interest for their potential to dramatically accelerate inferen…
We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning
Runqi Qiao, Qiuna Tan, Peiqing Yang +11
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various tasks, but still struggle with complex mathematical reasoning. Existing research p…
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models
Wonjun Kang, Kevin Galim, Yuchen Zeng +3
State Space Models (SSMs) have emerged as efficient alternatives to Transformers, mitigating their quadratic computational cost. However, the application of Parameter-Efficient Fin…
DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Tong Xie, Yuwei Wan, Yixuan Liu +8
Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces. Traditional solutions, such as hig…