From the 1 of 10 linked papers with an AI index.
10 papers
ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization
Yongge Ma, Guoan Wang, Feiyu Wang +5
Post-training quantization (PTQ) is widely used to reduce the memory and computational cost of large language models. Existing PTQ methods typically obtain an initial quantized mod…
Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding
Eileen Ye, Jiawen Tao, Yaoming Li +7
Long-running multi-turn interactions with chatbots and agents are now common, and a correct response often depends on remembering earlier details, tracking later revisions, identif…
Beyond Rephrasing: Book-Level Organization Improves Synthetic Textbook Data for Mid-Training
Jiawen Tao, Miao Peng, Yaoming Li +7
The paper introduces a pipeline that creates synthetic textbooks by clustering source material, planning hierarchical tables of contents, and assembling sections into full books, s…
RealClawBench: Live OpenClaw Benchmarks from Real Developer-Agent Sessions
Zongwei Lv, Zhewen Tan, Yaoming Li +7
Agent benchmarks should reflect what users actually ask deployed agents to do, yet existing benchmarks often miss key realism properties of real developer-agent sessions. We introd…
A Primer in Post-Training Reasoning Data: What We Know About How It Works
Yaoming Li, Guangxiang Zhao, Qilong Shi +3
Post-training has become a primary driver of recent progress in large reasoning models, and reasoning data are often the key variable determining whether this stage succeeds. Work…
ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression
Yilun Yao, Jiaming Pan, Elsie Dai +3
Mixture-of-Experts (MoE) language models reduce per-token computation but still require storing and serving all experts, making deployment memory-intensive. Existing post-training…