Off the frontier API: distillation, graded SFT, and RL for agents

2026-06-13 · Source: Alex Strick van Linschoten · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, long

Summary

Alex Strick van Linschoten's June 13, 2026 article, "Off the frontier API: distillation, graded SFT, and RL for agents," explores why organizations might train their own models instead of relying on expensive frontier APIs. Key motivations include mitigating vendor lock-in and the risk of API degradation (exemplified by Claude Fable's takedown), escalating costs for LLM-driven use cases despite per-token price drops, and critical privacy concerns in sectors like medicine and law. The author outlines three credible strategies for advanced structured data extraction: distilling a powerful teacher model, fine-tuning on graded production traces from an existing agent, and employing reinforcement learning with a scoring mechanism. Each approach offers distinct trade-offs in terms of data sourcing, effort, cost, and performance ceiling, with RL potentially surpassing teacher model capabilities.

Key takeaway

For AI/ML Directors evaluating LLM strategy, consider transitioning from exclusive frontier API reliance to custom model training for agent systems. This shift addresses escalating costs, privacy mandates, and vendor lock-in risks. Explore distillation for data generation, graded SFT for leveraging existing high-quality outputs, or reinforcement learning for potentially superior, cost-effective solutions, especially for stable, high-volume use cases like complex structured data extraction.

Key insights

Training custom models via distillation, graded SFT, or RL mitigates frontier API risks, costs, and privacy issues.

Principles

• Frontier API dependencies carry risks of removal or degradation.
• Custom model training requires significant investment in expertise and maintenance.
• Reinforcement Learning can exceed teacher model capabilities with effective grading.

Method

For advanced data extraction, options include distilling a teacher model to label data for SFT, grading existing agent traces for SFT, or using RL with an unsupervised scoring function to iteratively improve model attempts.

In practice

• Use distillation to generate training data from a powerful teacher model.
• Grade existing agent production traces to select high-quality SFT examples.
• Implement a multi-faceted grading function for RL to score model outputs.

Topics

• Reinforcement Learning
• Supervised Fine-Tuning
• Model Distillation
• LLM Agents
• API Lock-in
• Data Privacy

Code references

• strickvl/hinbox

Best for: Machine Learning Engineer, AI Engineer, Director of AI/ML

Related on AIssential

• Training Agents 2: Live tutorial on model distillation for training custom agents. — Model distillation transfers capabilities from large teacher models to smaller student models using various on-policy, off-policy, and self-distillation techniques.
• What and When to Distill: Selective Hindsight Distillation for Multi-Turn Agents — SERL selectively reweights RL objectives using environment feedback to improve credit assignment in multi-turn LLM agents.
• Capability Self-Assessment: Teaching LLMs to Know Their Limits — Reinforcement learning effectively teaches large language models to recognize their limitations, outperforming supervised fine-tuning.
• Demystifying On-Policy Distillation: Roles, Pathologies, and Regulations — OPD acts as an exploration catalyst, effective only with high-quality, well-regulated guiding signals.
• Keep Policy Gradient in Charge: Sibling-Guided Credit Distillation for Long-Horizon Tool-Use Agents — Direct self-distillation can degrade tool-use by misaligning with verifier rewards; distillation should guide credit, not replace policy gradient.
• SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning — Self-evolving on-policy distillation converts hindsight skills from trajectories into dense token-level supervision for agentic RL.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Alex Strick van Linschoten.