4 papers
Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning
Kehao Zhang, Shangtong Gui, Sheng Yang +2
Long-context LLMs and Retrieval-Augmented Generation (RAG) systems process information passively, deferring state tracking, contradiction resolution, and evidence aggregation to qu…
Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning
Yang Zhou, Sunzhu Li, Shunyu Liu +11
Recent advances in Large Language Models (LLMs) have underscored the potential of Reinforcement Learning (RL) to facilitate the emergence of reasoning capabilities. Despite the enc…
ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization
Sunzhu Li, Zhiyu Lin, Shuling Yang +2
Large Reasoning Models (LRMs) are powerful, but they still suffer from inefficient and off-target reasoning. Currently, training-free methods are limited to either rigid heuristics…
Make Domain Shift a Catastrophic Forgetting Alleviator in Class-Incremental Learning
Wei Chen, Yi Zhou
In the realm of class-incremental learning (CIL), alleviating the catastrophic forgetting problem is a pivotal challenge. This paper discovers a counter-intuitive observation: by i…