Topic
Low Rank Adaptation
5 episodes
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Beam is now available — Reflection
Vince and Ava talk through Reflection's Beam becoming available as an open-weight release, focusing on what changes now that the model, serving stack, and customization tooling are all shipping together. They stay skeptical about vendor-reported benchmarks, but land on the real story being the adoption path: a model you can actually download, serve, and adapt across a surprisingly broad ecosystem.
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NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning | NVIDIA Technical Blog
NVIDIA drops Nemotron 3.5 Super VL — a 120B-parameter open-weight multimodal MoE model built for specialized enterprise agents. Jessica and Cathy dig into the real argument: post-training on your own data beats the frontier on your specific task, and the efficiency story (40x inference cost reduction, 10x latency) is where the product actually lives.
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Overview: Supervised Fine Tuning
We finally slow down and make supervised fine-tuning click, because we keep leaning on S F T like everyone already has the whole shape of it. We build it from the apprentice-and-worked-examples picture into the actual training loop, the examples, and the trade-offs.
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Stanford Researchers Introduce TRACE: A Capability Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
Stanford's TRACE system converts recurring agent failures into targeted synthetic training environments, using LoRA experts and MoE routing to close specific capability gaps without retraining the whole model.
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Introducing OpenRL: A self Hosted post training API for fine tuning LLMs | Google Open Source Blog
Justy and Cody discuss Google’s OpenRL, a self-hosted post-training API that tries to separate RL research loops from the Kubernetes and GPU infrastructure underneath them.