29 citations · 60 across the 60 of their papers we have counts for
10 papers · 1 filter
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations
Akshay Malhotra, Nicolas Zilberstein, Shahab Hamidi-Rad
As large, powerful pre-trained models become more prevalent, there is growing interest in developing methods that allow different neural networks to be seamlessly combined with lit…
CompassPlay: Rewarding the Proposer for Where It Moves the Solver
Sophia Xiao Pu, Ximeng Sun, Jiang Liu +4
In self-play, a proposer generates verifiable tasks to train a solver. Proposer rewards often depend on the solver's success rate, but equally difficult tasks can differ in their t…
Learnable Permutation for Structured Sparsity on Transformer Models
Zekai Li, Ji Liu, Guanchen Li +5
Structured sparsity has emerged as a popular model pruning technique, widely adopted in various architectures, including CNNs, Transformer models, and especially large language mod…
APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation
Yuzhen Zhou, Jiajun Li, Yusheng Su +15
Reinforcement learning (RL) has become a cornerstone in advancing large-scale pre-trained language models (LLMs). Successive generations, including GPT-o series, DeepSeek-R1, Kimi-…
Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
Shuai Wang, Zhenhua Liu, Jiaheng Wei +3
We present Athena-PRM, a multimodal process reward model (PRM) designed to evaluate the reward score for each step in solving complex reasoning problems. Developing high-performanc…
Zebra-Llama: Towards Extremely Efficient Hybrid Models
Mingyu Yang, Mehdi Rezagholizadeh, Guihong Li +2
With the growing demand for deploying large language models (LLMs) across diverse applications, improving their inference efficiency is crucial for sustainable and democratized acc…