Recursive Self-Improvement via AI for Chip Design & Chip Design for AI - Ricursive Intelligence
- Rather than investing in a brute-force RL climb, we carefully iterated on our SFT and RL recipes with a focus on compute efficiency.
- We opted not to buy any posttraining data, instead devising our own synthetic data pipelines, quality filtering, and coverage metrics.
- Our mixed RL recipe uses truncated importance sampling, score centering, and linear length penalty with high-quality long-horizon synthetic environments.
Unverified
- Rather than investing in a brute-force RL climb, we carefully iterated on our SFT and RL recipes with a focus on compute efficiency.
- We opted not to buy any posttraining data, instead devising our own synthetic data pipelines, quality filtering, and coverage metrics.
- Our mixed RL recipe uses truncated importance sampling, score centering, and linear length penalty with high-quality long-horizon synthetic environments.
Sources: Ricursive