Excited to share our new pre-print https://t.co/FtT3hkHKeG
We train a digital agent that solves diverse day-to-day tasks from the AppWorld benchmark by interacting with its stateful environment using API calls. AppWorld is hard! The previous best open-weight agent (Llama 3 70B) reached only a 7% success rate on the hardest test split. Our RL algorithm, LOOP - a PPO variant with Monte Carlo baselines - achieves a 45.7% success rate, 24% over the base Qwen 2.5 32B, and 9% higher than a much larger OpenAI o1.
We report a model that can go from natural language instructions, to robot actions, to synthesized molecule with an LLM. We synthesized catalysts, a novel dye, and insect repellent from 1-2 sentence instructions. This has been a seemingly unreachable goal for years! 1/3