RL orchestration: how a 7B model routes tasks across GPT-5, Claude, and Gemini
Every LangChain pipeline your team hardcodes starts breaking the moment the query distribution shifts — and it always shifts. That bottleneck is what Sakana AI set out to eliminate.
Researchers at Sakana AI have introduced the "RL Conductor," a small language model trained via reinforcement learning to automatically orchestrate a diverse pool of worker LLMs. Conductor dynamically analyzes inputs, distributes labor among workers, and coordinates among agents.
This automated coordination achieves state-of-the-art results on difficult reasoning and coding benchmarks, outperforming individual frontier models like GPT-5 and Claude Sonnet 4 as well as expensive human-designed multi-agent pipelines. It achieves this performance at a fraction of the cost and with fewer API calls than competitors. RL Conductor is the backbone of Fugu, Sakana AI’s commercial multi-agent orchestration service.
The limitations of manual agentic frameworks
Large language models have strong latent capabilities. But tapping these capabilities to their fullest is a...
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