GRPO

From RL to IRL — Gaurav Mishra, Amazon AGI Lab

Aug 14, 2026 · 17:46

Gaurav Mishra of the Amazon AGI Lab explains why RL for computer-use agents works in games but breaks in real life, and how his team turns failures into training data. He shows early browser-training runs where an agent guesses its password and locks the account, and clicks a sponsored button styled like the submit button, landing elsewhere. The talk catalogs partial observability, irreversibility, expiring credentials, and ambiguous success, then proposes flight-school sandboxes, process reward models, calibrated confidence, and adversarial tasks. A later trajectory shows the agent recognizing the sponsored button, refusing to guess the password, and handing off to the user, echoing the talk's point that the difference between a demo and a product is what happens after the first failed click.

Scaling Compute on Context — Jack Morris, Engram

Aug 12, 2026 · 19:42

Jack Morris of Engram frames scaling compute on context as the pursuit of depth in AI, contrasting it with the breadth of public-data pre-training. He argues models trained on public data know nothing about your emails, meetings, or company, and that with a fixed private corpus, compute is the only scalable axis. He critiques naive fine-tuning (loss 0.00001 on 10K financial reports then collapse), KV compaction, on-policy distillation, and synthetic continued pretraining, noting each hits a synthetic data wall. The goal is self-improvement like AlphaGo, where better models generate harder training questions, enabling indefinite compute scaling on your context.