Beyond LoRA: Can you beat the most popular fine-tuning technique?
Beyond LoRA: Can you beat the most popular fine-tuning technique?
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Import AI 461: "Alignment is not on track"; FrontierCode; and synthetic research interns
Where are your agents right now?
RL without TD learning
In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer. Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks. We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal difference (TD) learning. Problem setting: off-policy RL Our problem setting is off-policy RL. Let’s briefly review what this means. There are two classes of algorithms in RL: on-policy RL and off-policy RL. On-policy RL means we can only use fresh data collected by the current policy. In other words, we have to throw away old data each time we update the policy. Algorithms like PPO and GRPO (and policy gradient meth
Unlocking the potential of vision language models on satellite imagery through fine-tuning
Unlocking the potential of vision language models on satellite imagery through fine-tuning
Mistral Fine-tuning Hackathon
We are thrilled to announce the Mistral AI fine-tuning hackathon, a virtual experience taking place from June 5 - 30, 2024.