From the 1 of 15 linked papers with an AI index.
15 papers
DASH: Divergence-Adaptive Supervision Horizons for On-Policy Self-Distillation of Reasoning Models
ZhiYan Hou, Xinyu Tang, Hongyan An +9
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models using automatically verifiable outcome signals, but these signals…
Continual Learning in Transition
Zhiyan Hou, Dan Zhang, Tao Feng +11
Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.g., training strategies, architect…
ReLoop-UME: Recurrent Depth with Learnable Retrieval Registers for Universal Multimodal Embedding
Shijie Wang, Xiangzhao Hao, Yueti Li +3
Universal multimodal embedding (UME) maps heterogeneous multimodal inputs into a shared embedding space. Existing UME models either form embeddings through single forward encoding…
Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
Xinyu Tang, Qianggang Cao, Yurou Liu +13
The paper introduces a training pipeline that scales zero‑reinforcement‑learning to a trillion‑parameter language model, revealing emergent chain‑of‑thought reasoning abilities and…
GraphPO: Graph-based Policy Optimization for Reasoning Models
Yuliang Zhan, Xinyu Tang, Jian Li +7
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard paradigm for enhancing the capability of large reasoning models. RLVR typically samples responses indepe…
SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research
Xiaochong Lan, Pu Ning, Quan Chen +8
Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inhe…