Introducing Reinforcement Learning from Collective Rewards (RLCR). RLCR is a recipe that trains language models as agent organisations, which share a message board and workspace. We use it to train Avalon-M1-Mini (120B) and study its behaviour. We find a number of interesting results, including: 🛠️ Learnt organisational hierarchies and divisions of labour 🕵️ Increased collective verification, through hires of verifiers and testers 👥 The rise of collective thinking, where agents think more about other agents and how to help them We believe RLCR can provide a laboratory for studying collective intelligence and multi-agent safety. Blog post and technical report 👇

A new frontier for on-device speech. We're open-sourcing Phonon, a suite of TTS models under 50m parameters, setting a new standard for naturalness and accuracy across languages, with the broadest inference support to date. Phonon-31m is available under Apache 2.0. gradium.ai/phonon🧵

We've spent the last several months scaling our compute footprint right here in the U.S., and we got to see some of it up close at the @CrusoeAI and Redwood Materials site in Nevada. Thanks @Electron_Cowboy and @ChaseLochmiller for the tour! Excited to keep building with Crusoe to bring our users day-zero models and full ZDR, with top performance. We look forward to sharing more soon 👀

Neoclouds can't just lease GPUs. They need to own the full agent inference platform: open-weight models (SGLang), fine-tuned models (Miles, River), and secure sandboxes (SPACE). We look forward to working with Coreweave, Akamai, Crusoe, and more partners.