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Articles by Raymond Cano
Introducing the Baseten Loops SDK
The RL post-training moment Over the past year, open-source models have continued to scale their architectures and intelligence. Demand for post-training has surged to specialize these models through supervised finetuning (SFT) and reinforcement learning (RL). As ML teams build more advanced main agents, voice systems, and long-horizon reasoning systems, RL is quickly becoming the default method to achieve frontier-level, task-specific performance.
Baseten Training: an autoresearch substrate
When Andrej Karpathy released autoresearch, it formalized something our researchers were already doing informally: humans define the research program, and an AI agent runs the experiments. We've been running this loop internally for the past few months, pointed at Baseten Training, and it has become a core part of how we work. That loop has driven our KV cache compaction research, our work rightsizing Qwen3 Coder for long sequence lengths, and several experiments we'll be sharing soon.
Train AI Models When You Want. Deploy on Ultra Performant Infrastructure. Baseten Training Is GA.
Training is Now GA Today, we’re excited to announce that Baseten Training is now generally available (GA) to all businesses on Baseten. It delivers tools such as caching, checkpointing, deploy-from-checkpoint, and new training recipes, making it the most agile training infrastructure for developers.
Create custom environments for deployments on Baseten
Developers want the ability to test production workflows and workloads without the consequences of failing in production.
Introducing canary deployments on Baseten
We’re excited to launch canary deployments, enabling you to gradually shift traffic to new model deployments over custom time windows! Rolling out new deployments is a fraught process, especially when it comes to complex generative AI models. From the model quality to performing at scale and interacting with other moving parts in your pipeline, there’s always a non-negligible risk that something could go wrong.
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