41 summarised stories about Multi-Agent Systems, each linking back to the original source. Browse all topics →
OpenAI 7 years ago 35
Agents trained in a simulated hide-and-seek environment discovered six distinct strategies and counterstrategies through multi-agent interaction without explicit instruction. The agents progressed from simple behavior to increasingly complex tool use within a single environment. This demonstrates that competitive multi-agent training can generate emergent complexity without direct supervision or predetermined task design.
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TLDRocket
16 October 2026
AI’s center of gravity this week moved toward something less glamorous than headlines about “smarter” models: scaling the stuff models actually learn from. AI4Bharat and NVIDIA released Nemotron-pretraining-Indic-v1, backed by an open, GPU-accelerated synthetic data generation pipeline for 22 Indian languages. The corpus is massive—about 2.8 trillion tokens—split across 2,601.0 billion native-script tokens and 224.6 billion romanized tokens. The practical pitch is even sharper: builders can reuse not just the data, but the full prompt/quality/repair recipe, then continue-pretraining to push results on Nemotron 3.5 Super VL, with the promise that an Indic continued-pretraining stage helps benchmarks.
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