🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Hidden gem: The Cohere Labs speaker series. Every week you can just drop into a call where some of the best ML/AI researchers present their latest findings. From Microsoft Research's Jianwei Yang on multimodal agents, to New York University's Eugene Vinitsky 🍒🦋 on Self-Play, to up-and-coming

Our paper M-RewardBench got accepted to ACL main: arxiv.org/abs/2410.15522 We construct the first-of-its-kind multilingual RM evaluation benchmark and leverage it to look into the performances of several Reward Models in non-English settings along w/ other interesting insights.

How can we make language models more flexible to adapt to new languages after pretraining? 🌏 🧠 Our latest work investigates whether a tokenizer trained on more languages than the pretraining target can improve language plasticity without compromising pretraining performance.

🚨New pretraining paper on multilingual tokenizers 🚨 Super excited to share my work with Cohere Labs: One Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual Tokenizers

An excellent work by Diana Abagyan💎 We show that a "universal" tokenizer, covering more than just primary languages, greatly boosts new language adaptation without hurting pretraining performance 🚀 A very critical study for multilingual LLMs given huge cost of pretraining🔥

Thanks AK for the spotlight on our work I really believe strongly in this wider direction — of taking the pressure off everyday users to be master prompt engineers and inferring controllability directly from tasks.

Thanks for the feature AK ! 😄🤝

Can we train models for better inference-time control instead of over-complex prompt engineering❓ Turns out the key is in the data — adding fine-grained markers boosts performance and enables flexible control at inference🎁 Huge congrats to Daniel D'souza  for this great work

🤹 How do we move away from complicated and brittle prompt engineering at inference for under-represented tasks?🤔 🧠 Our latest work finds that optimizing training protocols improves controllability and boosts performance on underrepresented use cases at inference time 📈

🤝Arbitration is the future 🤝 “Why rely on a single teacher 🧑🏻‍🏫 when you can synthetically generate a much higher quality dataset by relying on specialized teacher models? 🧑🏻‍🏫👩‍🏫👨🏿‍🏫” Check out this fantastic summary of our recently accepted ACL 2025 work ✨

We’re proud to have released 9 open models — all built to support research, experimentation, and real-world impact. 🌎 These models reflect our commitment to building powerful, accessible tools that can accelerate progress across machine learning and beyond.

Yooo 👀 and there’s pizza!?! 🍕