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913: LLM Pre-Training and Post-Training 101 — with Julien Launay

913: LLM Pre-Training and Post-Training 101 — with Julien Launay

Julien Launay, CEO of Adaptive ML, discusses the evolution of Large Language Model (LLM) training, detailing the critical shift from pre-training to post-training with Reinforcement Learning (RL). He explains the nuances of RL feedback mechanisms (RLHF, RLEF, RLAIF), the role of synthetic data, and how his company provides the "RLOps" tooling to make these powerful techniques accessible to enterprises. The conversation also explores the future of AI, including scaling beyond data limitations and the path to a "spiky" AGI.

Introducing GPT-5

Introducing GPT-5

OpenAI introduces GPT-5, a significant upgrade focused on expert-level reasoning, agentic capabilities, and real-world utility, particularly for developers and enterprises. The model introduces a new reasoning paradigm, "software on demand" capabilities, and state-of-the-art performance on coding, reasoning, and long-context benchmarks. The launch also includes major updates to the ChatGPT application and a powerful new API for developers.

How to look at your data — Jeff Huber (Choma) + Jason Liu (567)

How to look at your data — Jeff Huber (Choma) + Jason Liu (567)

A detailed summary of a talk by Jeff Huber (Chroma) and Jason Liu on systematically improving AI applications. The talk covers using fast, inexpensive evaluations for retrieval systems (inputs) and applying structured data analysis and clustering to conversational logs (outputs) to derive actionable product insights.

[Full Workshop] Building Metrics that actually work — David Karam, Pi Labs (fmr Google Search)

[Full Workshop] Building Metrics that actually work — David Karam, Pi Labs (fmr Google Search)

This workshop, led by former Google product directors, introduces a methodology for building reliable and tunable evaluation metrics for LLM applications. It details how to create granular 'scoring systems' that break down complex evaluations into simple, objective signals, and then use these systems for model comparison, prompt optimization, and online reinforcement learning.

Chelsea Finn: Building Robots That Can Do Anything

Chelsea Finn: Building Robots That Can Do Anything

Developing general-purpose robots requires a shift from specialized, single-task systems to broad foundation models. This is achieved through a combination of large-scale, diverse, real-world data collection and a specific training methodology: pre-training on all available data and then fine-tuning on a curated, high-quality subset of demonstrations. This recipe, combined with architectural innovations to preserve the capabilities of Vision-Language Model (VLM) backbones, enables robots to perform complex, long-horizon tasks, generalize to unseen environments, and respond to open-ended human instructions.

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