Thrilled to share our new preprint on Reinforcement Learning for Reverse Engineering (RLRE) 🚀 We demonstrate that human preferences can be reverse engineered effectively by pipelining LLMs to optimise upstream preambles via reinforcement learning 🧵⬇️

I'm excited to share the tech report for our @cohere.com @cohereforai.bsky.social Command A and Command R7B models. We highlight our novel approach to model training including self-refinement algorithms and model merging techniques at scale. Read more below! ⬇️

I really enjoyed my MLST chat with Tim @neuripsconf.bsky.social about the research we've been doing on reasoning, robustness and human feedback. If you have an hour to spare and are interested in AI robustness, it may be worth a listen 🎧 Check it out at youtu.be/DL7qwmWWk88?...

Check out @lisaalaz.bsky.social's internship work with us @cohere.com questioning the rationale behind rationales 🔥

Super excited to see PRISM recognised as a #NeurIPS2024 best paper. This was an incredible large-scale effort by @hannahrosekirk.bsky.social and fantastic collaborators. If you're interested in human feedback, check it out, there are 100+ pages of detailed insights! 🔥

Our paper PRISM alignment won a best paper award at #neurips2024! All credits to @hannahrosekirk.bsky.social A.Whitefield, P.Röttger, A.M.Bean, K.Margatina, R.Mosquera-Gomez, J.Ciro, @maxbartolo.bsky.social H.He, B.Vidgen, S.Hale Catch Hannah tomorrow at neurips.cc/virtual/2024/poster/97804

Excited to reveal Genie 2, our most capable foundation world model that, given a single prompt image, can generate an endless variety of action-controllable, playable 3D worlds. Fantastic cross-team effort by the Open-Endedness Team and many other teams at Google DeepMind! 🧞

Looking forward to @neuripsconf.bsky.social #NeurIPS #NeurIPS2024 in Vancouver next week! ❄️ Reach out (or pop by the @cohere.com booth) if you want to chat about human feedback, robustness and reasoning, prompt optimisation, adversarial data, glitch tokens, evaluation, or anything else!

Sparks of multi-hop reasoning ✨

Fun to see Douwe's Dynabench plot continue to inspire new groundbreaking benchmarking work!

🚨 LLMs can learn to reason from procedural knowledge in pretraining data! 🚨 I particularly enjoy research where the evidence contradicts our initial hypothesis. If you're interested in LLM reasoning, check out the 60+ pages of in-depth work at arxiv.org/abs/2411.12580

We launched Judge Arena with @huggingface.bsky.social @clefourrier.bsky.social - a platform that lets you easily compare models as judges side-by-side and vote for the best evaluation Check out the live leaderboard and start voting now 🤗