Diego Francisco Valenzuela Iturra
7.5K posts

Diego Francisco Valenzuela Iturra
@diegovogeid
Coffee, Music and Deep Learning






Today's the deadline to apply for YC Summer 2026. ycombinator.com/apply


Banger paper from Meta FAIR. They introduce Autodata, an agentic data scientist that builds high-quality training and evaluation data autonomously. The headline result: on a CS research QA task, an Agentic Self-Instruct loop produces a 34-point gap between weak and strong solvers (43.7% vs 77.8%), while standard CoT Self-Instruct on the same setup produces a 1.9-point gap (71.4% vs 73.3%). The agent generates questions that actually discriminate between models. The method: An orchestrator LLM directs a challenger agent to generate examples grounded in domain documents. A weak and a strong solver attempt them, a judge scores the outputs, and the orchestrator analyzes the failures and prompts the challenger to regenerate from new angles until quality thresholds are met. The system also meta-optimizes itself. An outer loop tunes the agent's instructions based on which harness changes lift validation pass rate. Over 126 accepted iterations, validation pass rate climbed from 12.8% to 42.4%. They processed 10,000+ CS papers and produced 2,117 quality-filtered QA pairs. Existing self-instruct pipelines do not control data quality. Autodata reframes data generation as an agent loop, spend more inference compute and the data gets harder, which gives downstream RL a real lift. Blog: facebookresearch.github.io/RAM/blogs/auto… Learn to build effective AI agents in our academy: academy.dair.ai
















