4 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
Utility-inspired Reward Transformations Improve Reinforcement Learning Training of Language Models
Roberto-Rafael Maura-Rivero, Chirag Nagpal, Roma Patel +1
Current methods that train large language models (LLMs) with reinforcement learning feedback, often resort to averaging outputs of multiple rewards functions during training. This…
Jackpot! Alignment as a Maximal Lottery
Roberto-Rafael Maura-Rivero, Marc Lanctot, Francesco Visin +1
Reinforcement Learning from Human Feedback (RLHF), the standard for aligning Large Language Models (LLMs) with human values, is known to fail to satisfy properties that are intuiti…
Soft Condorcet Optimization for Ranking of General Agents
Marc Lanctot, Kate Larson, Michael Kaisers +7
Driving progress of AI models and agents requires comparing their performance on standardized benchmarks; for general agents, individual performances must be aggregated across a po…