4 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
SchemaFill: Efficient LLM Tool Calling via Slot-Parallel Speculative Decoding
Zhi-Kai Chen, Song-Yan Li, De-Chuan Zhan +1
LLM agents interact with external systems by generating structured tool calls. Given a user request, conversational context, and a catalog of tool schemas, a tool-calling model mus…
Dense Supervision, Sparse Updates: On the Sparsity and Geometry of On-Policy Distillation
Guo Yu, Wenlin Liu, Yulan Hu +3
On-policy distillation (OPD) has recently become a prominent post-training recipe by combining two desirable ingredients: on-policy student-generated trajectories and dense token-l…
: Generalist Value Model as a Prior for Sparse RL Rollouts
Yi-Kai Zhang, Yueqing Sun, Hongyan Hao +4
In Reinforcement Learning with Verifiable Rewards (RLVR), constructing a robust advantage baseline is critical for policy gradients, effectively guiding the policy model to reinfor…
Multimodal Tabular Reasoning with Privileged Structured Information
Jun-Peng Jiang, Yu Xia, Hai-Long Sun +6
Tabular reasoning involves multi-step information extraction and logical inference over tabular data. While recent advances have leveraged large language models (LLMs) for reasonin…
UniCA: Unified Covariate Adaptation for Time Series Foundation Model
Lu Han, Yu Liu, Lan Li +9
Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their a…
MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning
Hai-Long Sun, Da-Wei Zhou, Hanbin Zhao +3
Class-Incremental Learning (CIL) requires models to continually acquire knowledge of new classes without forgetting old ones. Despite Pre-trained Models (PTMs) have shown excellent…