Google Just Achieved True Intelligence With New AI

Google just fused two opposite training methods into one system — Supervised Reinforcement Learning (SRL) — and made small 7B models earn intelligence with dense, step-wise rewards. In tests on s1K 1.1 and code tasks, SRL beat standard SFT and held up when no rollout was correct. The winning recipe: SRL first, then RLVR. Meanwhile, Gemini 2.0’s multi-agent “AI co-scientist” helped researchers surface drug candidates for liver fibrosis and independently reconstructed a decade-long discovery about phage tail piracy in bacteria—in days.

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🧠 What You’ll See (sources):
Google SRL paper (arXiv): From Expert Trajectories to Step-wise Reasoning
https://arxiv.org/pdf/2510.25992

• RLVR background (arXiv): Reinforcement Learning with Verifiable Rewards
https://arxiv.org/abs/2506.14245

• Google Research blog: Accelerating scientific breakthroughs with an AI co-scientist
https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/

• Liver fibrosis study (Advanced Science, DOI): AI-Assisted Drug Re-Purposing for Human Liver Fibrosis
https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202508751

• PubMed record (Vorinostat details):
https://pubmed.ncbi.nlm.nih.gov/40946179/

• Preprint (liver fibrosis):
https://www.biorxiv.org/content/10.1101/2025.04.29.651320v1.full-text

• Tail piracy explainer (Imperial College news):
https://www.imperial.ac.uk/news/268213/microbial-piracy-uncovers-fight-drug-resistant-infections/

• Coverage (GEN / summary with AI angle):

Tail-Swapping “Pirate” Phages Expose New Route for AMR

🚨 Why It Matters:
Small AIs can now reason step by step without giant reward models, and multi-agent systems are doing real science—compressing years of discovery into days. This flips the script on scale vs. smarts.