4 citations · 4 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…