1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
World Modelling Improves Language Model Agents
Shangmin Guo, Omar Darwiche Domingues, Raphaël Avalos +2
Tool use in stateful environments presents unique challenges for large language models (LLMs), where existing test-time compute strategies relying on repeated trials in the environ…
ShiQ: Bringing back Bellman to LLMs
Pierre Clavier, Nathan Grinsztajn, Raphael Avalos +8
The fine-tuning of pre-trained large language models (LLMs) using reinforcement learning (RL) is generally formulated as direct policy optimization. This approach was naturally fav…
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study
Menglong Cui, Pengzhi Gao, Wei Liu +2
Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement. I…