1 citations · 1 across the 5 of their papers we have counts for
6 papers
MiMo-V2-Flash Technical Report
Core Team, Bangjun Xiao, Bingquan Xia +123
We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-…
The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding
Yifan Qian, Zhe Wen, Alexander C. Furnas +3
Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly diffusing into scientific practice, h…
Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation
Wenzhen Luo, Wei Guan, Yifan Yao +6
We introduce Falcon, a cross-domain Chinese text-to-SQL benchmark grounded in an enterprise-compatible dialect (MaxCompute/Hive). It contains 600 Chinese questions over 28 database…
Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models
Akhil Agnihotri, Rahul Jain, Deepak Ramachandran +1
Post-training LLMs with RLHF and preference optimization methods (e.g., DPO, IPO) has greatly improved alignment, yet these approaches assume a single objective. In reality, humans…
Best Policy Learning from Trajectory Preference Feedback
Akhil Agnihotri, Rahul Jain, Deepak Ramachandran +1
Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful approach for aligning generative models, but its reliance on learned reward models makes it vulnerable t…
Online Bandit Learning with Offline Preference Data for Improved RLHF
Akhil Agnihotri, Rahul Jain, Deepak Ramachandran +1
Reinforcement Learning with Human Feedback (RLHF) is at the core of fine-tuning methods for generative AI models for language and images. Such feedback is often sought as rank or p…