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 👇

Maybe important to repost this now that everyone is getting hacked If you have a VPS, I recommend you to get if off the public internet and into a Tailscale network and Cloudflare Tunnel ASAP It doesn't matter you have SSH key login, this is about exploits and vulnerabilities AI is finding There's 2 ways into a VPS: - via your site/app (like the PHP exploit of this week with a file upload hack) - via your server (like hacking into SSH or some open port) The site/app side you put on a Cloudflare Tunnel and then with the default Free Plan it has WAF security enabled by default and they jump on new security exploits fast, it means that the only way to reach your website is via the Cloudflare Tunnel, your server does not respond to port 80 or 443 anymore The server side you put on a Tailscale network which is like your own private network that nobody can access but your own devices (like your laptop, your phone and your VPS) kinda like a VPN Then you switch off ANY regular access to your VPS on the firewall, in my case I use Hetzner and you create a new firewall then empty it and that blocks everything (test it) How do Tailscale and Cloudflare Tunnel get in then? They don't. They go outbound. They make a tunnel to outside which sends and receives Now you've reduced your attack vector to Tailscale and your Cloudflare Tunnel, much safer, not perfect btw, but nothing is

Today we're introducing Fluid Speech Mini - the Best Open-Source speech model for on-device Dictation Fluid Speech Mini surpasses Whisper Large, and Parakeet in Accuracy in noisy environments, While being ~10x smaller than Whisper Large, and ~5x smaller than Parakeet. So you can run it for on-device accurate dictation on your MacBook, or even iPhone 11. More info on Fluid Speech Mini below!

Day 16/ You can now cache for 12h in the API and for logged in users across Codex & Work we keep cache forever and you don’t have any penalty for coming back to an old conversation or resuming after a few hours and are now guaranteed to hit the cache.

Mentre aspetto che finisca il training di Mario-1 che impiega diverse ore, ho scaricato Emma-6 da Hugging Face e l'ho messo alla prova. ARC 31% HellaSwag 41,5% BoolQ 43,5% TruthfulQA 30,5% GSM8K 41,5% Media 37,6% L'AI italiana merita di più. Mario-1 arriva a breve. #AISovrana