FireAct (Language Agent Fine-tuning)
Explores fine-tuning LLMs specifically for language-agent use, demonstrating consistent gains over prompting alone.
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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.