🚀 #RFT vs. #SFT: When to Use Each for Maximum Impact #DeepSeek -R1 made #Reinforcement #FineTuning (RFT) the hot new thing—but is it better than #Supervised Fine-Tuning (SFT)? 🤔 Here’s when RFT wins: ✅ No labeled data? If you can verify correctness, RFT works. ✅ <100

Train your own DeepSeek-R1 with GRPO in Predibase: the first fully-managed serverless Reinforcement Fine-Tuning platform.

Today we're thrilled to announce the first end-to-end platform for Reinforcement Fine-Tuning. With just a dozen labeled data points, you can outperform #OpenAI o1 and #DeepSeekR1 on complex tasks. Built on the #GRPO methodology that DeepSeek-R1 popularized, our platform delivers

🚀 Launching Reinforcement Fine-Tuning (RFT) at Predibase - the first platform to fine-tune LLMs with just a few prompts & reward functions. No massive datasets needed, just GPT-4o & GPT-o1 beating performance made simple.

Huge release from @Predibase today -- the first end-to-end platform for Reinforcement Fine-Tuning Bringing the techniques that power DeepSeekR1 to any open source model and data

Fine-tuning is great for adapting LLMs to specific tasks, but what if you don’t have much data? Starting today, you can use the world’s first end-to-end Reinforcement Fine-Tuning (RFT) Platform within Predibase and train models with zero data! We’ve enhanced GRPO, the

As we all know by now, reasoning models often generate longer responses, which raises compute costs. Now, this new paper (arxiv.org/abs/2504.05185) shows that this behavior comes from the RL training process, not from an actual need for long answers for better accuracy. The RL

🐳 AI teams are testing DeepSeek—but nobody agrees on when to use it In our recent survey of 500+ AI professionals, DeepSeek-R1 is getting serious attention—but it's far from mainstream. Here’s what we uncovered: 📊 57% of teams have experimented with DeepSeek-R1 ⚠️ Only 3%

Supervised & Reinforcement fine-tuning in LLMs, clearly explained (with visuals):

🚀 Serve and fine-tune #Qwen3 — in your cloud or ours with blazing fast #inference speeds! No need to share your data. 🚀 Qwen 3 is the latest #opensource LLM dominating the leaderboards. Don't get left behind! Now you can serve and customize the latest Qwen models instantly

New Course: Reinforcement Fine-Tuning LLMs with GRPO! Learn to use reinforcement learning to improve your LLM performance in this short course, built in collaboration with @Predibase, and taught by Travis Addair, its Co-Founder and CTO, and Arnav Garg, its Senior Engineer and

It was an honor getting to work together with the DeepLearning.ai team and my colleague Arnav Garg on this course covering all things Reinforcement Fine-Tuning and GRPO. Similar to our last course on efficient LLM inference, we wanted to really drill into the intuition

I had a blast working with the DeepLearning.AI team and my colleague Travis Addair over the last few months to put this course together on Reinforcement Fine-Tuning with GRPO! We’ve tried to make this course as practical as possible and help you build intuition. Hope you enjoy!

🚀 Fresh off our hit DeepLearning.AI course on RFT + #GRPO, we’re going live! 🎙️ Let’s Talk Tokens: Live #AMA on Reinforcement Fine-Tuning with the Experts Who Built the Definitive Course! #RFT isn’t just research any more—it’s driving real-world GenAI with tighter feedback

Struggling with context management? Wish you could just stick it all in your model? We’ve integrated Cartridges, a new method of leveraging sleep-time compute for learning long contexts, into Tokasaurus, an inference engine optimized for high-throughput 🧵

Big news! We will be joining @RubrikInc to accelerate agentic AI adoption from pilot to production at scale! ⚡️ Together, we can deliver radical simplicity in models and data. This is an exciting next step in our journey. More from Dev Rishi here: pbase.ai/45yUL2O

🧠 Join the 10k developers supercharging their #LLM skills with Reinforcement Fine-tuning—and it's free! 🧠 Reinforcement Fine-Tuning (#RFT) and #GRPO are fast becoming popular techniques to teach LLMs how to reason. We teamed up with DeepLearning.AI to build the definitive