TL;DR
A technical report on Fugu and Fugu-Ultra, language-model orchestrators trained to construct query-adaptive scaffolds for teams of heterogeneous LLM agents.
Why it matters
This is scaling through coordination rather than a single policy. The training system combines large-scale fine-tuning, evolutionary search, and reinforcement learning to decide how multiple agents should be assembled for each task.
Key findings
- 01
Fugu generates task-specific agentic scaffolds instead of applying one fixed multi-agent workflow to every query.
- 02
The report combines fine-tuning, evolutionary algorithms, and reinforcement learning with production infrastructure for multi-agent execution.
- 03
Reported benchmark gains mix model quality, orchestration, tool use, latency, and inference budget, so they should not be interpreted as a single-model scaling law.
Scaling dimensions
Models, methods & benchmarks
- Models
- Fugu, Fugu-Ultra
- Algorithms
- Reinforcement learning, Evolutionary algorithms, Multi-agent orchestration
- Benchmarks
- SWE-Bench Pro, Terminal Bench, LiveCodeBench, GPQA-Diamond, Humanity's Last Exam, CharXiv Reasoning
Topics