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The results show that targeted RL fine-tuning on high-quality, task-specific data can close — and reverse — a 231-billion-parameter gap in model size, at a training cost under $500, on a real financial reasoning benchmark.
The demo illustrates that Gemini's audio stack now spans transcription, expressive speech synthesis, real-time sound-to-sound interaction, and full-song music generation — all accessible through a unified API with tool-use integration.
The talk illustrates why standard code-level debugging is insufficient for agentic systems and presents a concrete framework — spanning telemetry, multi-scope evals, and automated analysis — for making nondeterministic AI agents production-ready.