4 citations · 6 across the 5 of their papers we have counts for
4 papers · 1 filter
TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding
Jihao Zhao, Qianyu Jiang, Shuaishuai Zu +6
Parallel drafters such as DFlash and DSpark reduce proposal latency, but early mismatches limit how much of each block can be accepted. Retaining alternatives introduces parent sta…
QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate
Jihao Zhao, Daixuan Li, Pengfei Li +3
The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowl…
A Survey of Reinforcement Learning for Large Reasoning Models
Kaiyan Zhang, Yuxin Zuo, Bingxiang He +36
In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontie…
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
MiniMax, :, Aili Chen +125
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combin…