To Train or Not to Train

2024-09-30 · Source: Tanay’s Newsletter · Field: Business & Management — Corporate Strategy & Leadership, Entrepreneurship & Start-ups, Project & Product Management · Depth: Intermediate, medium

Summary

AI application companies are increasingly integrating into the model layer, primarily through post-training on strong open-weights bases rather than pre-training from scratch. This approach offers significant benefits, including improved unit economics and reduced latency, with examples like Intercom's Fin Apex 1.0 reportedly running at one-fifth the cost and 0.6 seconds faster than competitors. It also enables differentiation by leveraging proprietary data, as seen with Cursor's Composer 2 and OpenEvidence's domain-specialized models. Furthermore, companies can develop specialized models for specific pipeline components that frontier labs do not prioritize, such as query rewriting or intent classification. However, a major risk is that rapid base-model releases, like OpenAI's GPT-5.x series, can quickly diminish the advantages of post-trained models. The decision to post-train is best made when a company has achieved product-market fit and accumulated sufficient proprietary data, with new infrastructure from vendors like Tinker and Prime Intellect's Lab lowering the entry barrier.

Key takeaway

For AI Product Managers evaluating custom model development, prioritize post-training specialized models only after achieving product-market fit and accumulating significant proprietary data. Focus on optimizing specific, "boring" pipeline components where frontier models underperform, rather than attempting to replace core reasoning. Begin building robust data collection and evaluation systems now, as this durable investment will allow your models to adapt to the accelerating pace of base model improvements.

Key insights

Post-training specialized models on open-weights bases offers AI application companies economic, latency, and differentiation advantages, despite rapid base model evolution.

Principles

• Proprietary data drives differentiation.
• Specialized models optimize specific pipeline parts.
• Rapid base model releases pose risks.

Method

Post-training involves supervised fine-tuning or RL on open-weights models, often for specialized tasks like query rewriting or intent classification, leveraging proprietary data and internal benchmarks.

In practice

• Develop internal benchmarks from real traces.
• Start with small, specialized pipeline models.
• Build data collection and evaluation systems.

Topics

• AI Application Development
• Post-training Models
• Open-weights Models
• Proprietary Data
• Model Specialization
• AI Infrastructure

Best for: Product Manager, CTO, VP of Engineering/Data, Director of AI/ML, AI Product Manager, Entrepreneur

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• ImportAI 449: LLMs training other LLMs; 72B distributed training run; computer vision is harder than generative text — LLMs can partially refine other LLMs, while post-training significantly boosts AI capabilities.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tanay’s Newsletter.