Agent LUMOS aims to address the limitations of closed-source language agents by providing an affordable, transparent, and reproducible alternative. The framework features a learnable architecture with planning, grounding, and execution modules for diverse tasks. LUMOS exhibits superior performance on held-out datasets compared to GPT-based agents and other open-source models. The annotations used in training are derived from existing benchmarks converted into a unified format suitable for agent training.
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