6 citations · 9 across the 14 of their papers we have counts for
4 papers · 1 filter
Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing
Tong Zheng, Chengsong Huang, Runpeng Dai +9
Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-traj…
Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Tong Zheng, Hongming Zhang, Wenhao Yu +7
Parallel thinking has emerged as a novel approach for enhancing the reasoning capabilities of large language models (LLMs) by exploring multiple reasoning paths concurrently. Howev…
CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Runpeng Dai, Linfeng Song, Haolin Liu +8
Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm for enhancing the reasoning ability of Large Language Models (LLMs). Yet current RLVR methods often exp…
Return of the Encoder: Maximizing Parameter Efficiency for SLMs
Mohamed Elfeki, Rui Liu, Chad Voegele
The dominance of large decoder-only language models has overshadowed encoder-decoder architectures, despite their fundamental efficiency advantages in sequence processing. For smal…