World's Fair 2026
Modern Post-Training: A Deep Dive — Will Brown, Prime Intellect
Overview
This talk delves into modern post-training techniques for AI models, focusing on the open-source Verifiers and Primed RL libraries. It emphasizes the evolution of these tools to handle increasingly complex agent use cases and the need for robust infrastructure to support large-scale, customized model training. The core thesis is that by providing flexible and powerful open-source toolkits, it becomes more accessible for engineers and builders to enhance existing models for their specific applications and workflows, fostering continuous improvement through iterative refinement.
Who should watch
- AI Engineers
- Product Managers
- Builders working with AI models
- Those interested in open-source AI research and infrastructure
- Individuals looking to improve model performance on specific tasks
- Teams needing to train and deploy custom AI models
Key takeaways
- Environments are a fundamental concept in post-training, serving as a language for specifying model behavior, encapsulating data and interaction scenarios, and defining scoring mechanisms.
- The Verifiers library has been overhauled into V1, decoupling tasks, harnesses, and runtimes to offer greater flexibility and composability for various agentic and traditional RL environments.
- Primed RL is an asynchronous, full-stack training framework designed for efficient, large-scale reinforcement learning, supporting custom algorithms and custom model training.
- Modern post-training workflows can integrate Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and on-policy distillation, with environments and evaluations serving as the gateway to these processes.
- Group rewards and reward shaping are crucial for simultaneously incentivizing multiple objectives, such as correctness and conciseness, by leveraging variance across multiple samples.
- Tools and user simulators, often managed via MCP, are increasingly important for complex applications, allowing for more realistic multi-turn interactions and training scenarios.
- The Verifiers V1 framework supports a flexible interception server pattern, enabling seamless transitions between training and inference modes and compatibility with various model APIs.
- Asynchronous RL is key to overcoming the long-tail distribution of agent task durations, preventing slow rollouts from bottlenecking overall progress and allowing for greater flexibility in managing compute resources.
Notable quotes
*Environments are a language for specifying what you want your model to do.*
*Evals are the thing that opens the door to post-training.*
*The point of this is to have flywheels that make everything get better.*
Unofficial community note. Prefer the recording for nuance.