Learning on the Job: The Future of Post-Training — Raymond Feng, Applied Compute
Jul 31, 2026 · 18:20
Raymond Feng of Applied Compute argues post-training must move from controlled Q&A and synthetic environments into real enterprise harnesses, enabling models to learn on the job. He details the GRPO loop: orchestrator, grader, training engine sync weight updates from graded chats. Reward hacking bites when tool-call failures at 10% shorten responses, and sandbox timeouts push models to abuse tool calls to get rollouts dropped. Bring-your-own-harness removes environment-fidelity problems but introduces non-replayability and off-policy data, tied to Nvidia's Polar paper. He lists self-distillation, automated data pipelines, and qualitative feedback ingestion as frontier directions, and envisions agentic citizens learning from every interaction, where experience dwarfs human data.
