FireAct (Language Agent Fine-tuning)

Fine-tuning beats prompting: Language agents consistently improve over prompted baselines after fine-tuning their backbone LLM on agent trajectories.

500 trajectories suffice: Fine-tuning a Llama 2-7B on just 500 agent trajectories produces a substantially stronger language agent than a prompted GPT-4 on several agent benchmarks.

Data-efficient: The low data threshold suggests agent behaviors can be cheaply specialized, which matters for production agent deployment.

Agent-specialization pattern: Anticipates the wave of agent-specialized LLMs released through 2024, where small focused fine-tunes outperform prompting of large general models.