913: LLM Pre-Training and Post-Training 101, with Julien Launay
🎯 Summary
Podcast Episode Summary: 913: LLM Pre-Training and Post-Training 101, with Julien Launay
This 75-minute episode features Julien Launay, co-founder and CEO of Adaptive ML, providing a deep dive into the methodologies behind creating and refining Large Language Models (LLMs), specifically contrasting the pre-training and post-training phases. The discussion moves from foundational AI training concepts to the cutting-edge role of reinforcement learning (RL) in modern model refinement and the commercial implications for enterprise AI.
1. Focus Area
The primary focus is on the lifecycle of Large Language Model (LLM) creation, detailing the distinct stages of Pre-training (foundational knowledge acquisition) and Post-training (alignment and specialization). Key technical concepts covered include various forms of Reinforcement Learning (RLHF, RLEF, RLAIF) used in post-training, and the shift in compute allocation between these two phases.
2. Key Technical Insights
- The Evolving Training Paradigm: Historically, pre-training (predicting the next token on massive web data) consumed the vast majority of compute. However, modern models (like Grok) are spending nearly as much on post-training, indicating a massive scaling up of alignment and refinement efforts.
- Plurality of Reinforcement Learning Signals: Post-training is increasingly driven by sophisticated feedback mechanisms beyond traditional Human Feedback (RLHF). These include Execution Feedback (RLEF), where models are rewarded based on verifiable outcomes (e.g., passing code tests, solving math problems), and AI Feedback (RLAIF), where a specialized model evaluates outputs, offering scalable alternatives to human annotation.
- Verification vs. Generation: A core principle discussed is that verification is often easier and more scalable than generation. Models can be highly effective at checking the correctness of another model’s output (RLAIF/RLEF) even if the initial human data used for comparison contains significant “noise” (low inter-rater agreement).
3. Business/Investment Angle
- Enterprise Customization via Adaptive ML: Launay’s company, Adaptive ML, focuses on making fine-tuned models easily accessible for enterprises using smaller, cost-efficient models, suggesting a market trend toward specialized, rather than monolithic, foundation models.
- The Value of Post-Training Data: The increasing investment in post-training signals that the competitive edge is shifting from simply having the largest pre-trained model to having the most effective, aligned, and specialized model, often achieved through proprietary, high-quality feedback loops.
- Scalability of Synthetic Data: The success of RLAIF and RLEF demonstrates a viable path for companies to generate massive amounts of high-quality training data internally, reducing reliance on expensive, slow human annotation pipelines for alignment.
4. Notable Companies/People
- Julien Launay: Guest, Co-founder/CEO of Adaptive ML, bringing expertise from Hugging Face and Light On.
- Grok (xAI): Mentioned as a recent example demonstrating the power of extensive post-training and reinforcement learning to achieve high benchmark scores (e.g., on law exams).
- Annotation Companies (Scale, Surge): Referenced in the context of traditional RLHF data sourcing, highlighting the limitations of human scalability.
5. Future Implications
The industry is moving toward a future where post-training is as critical, if not more so, than pre-training. This shift implies that innovation will increasingly focus on creating scalable, automated, and verifiable feedback environments (RL environments) to continuously tune models for specific tasks, moving beyond the initial, broad knowledge acquisition phase.
6. Target Audience
This episode is highly valuable for AI/ML Engineers, Data Scientists involved in model deployment, AI Product Managers, and Technology Strategists interested in the practical engineering and commercial realities of building and aligning state-of-the-art LLMs for real-world applications.
🏢 Companies Mentioned
💬 Key Insights
"Models currently cannot do this [conduct real-world experiments]. There is no way, you know, currently for a model to run biological tool to get in the scalable way."
"the bulk of the resources are going to be spent in the future on post-training, on reinforcement learning because the models are going to learn from trying again and again across, you know, billions of virtual environments and eventually, although in the real world, against trying, you know, their experiments, their own ideas."
"this is infinitely scalable because this is essentially the experience of the model in the real world."
"we go back to post-training where what's very interesting about post-training is that post-training enables model... it enables model to learn from experience."
"But I think I can answer the question of what's next and what's already actually the case, which is we go back to post-training where what's very interesting about post-training is that post-training enables model to learn from experience. So the model actually does something."
"There's this quote that I really like for reinforcement to describe reinforcement learning, which is that if you can measure it, you can optimize it. This is literally true actually."