General Agent: A Self-Evolving, Synthetic Agent Environment

The general-agent environment, now open-sourced as Environments Hub, is a synthetic platform designed for training capable agents by exposing them to a wide array of tasks and tools. It operates on a two-agent system: the Synthesizer, which creates tasks following a structured schema and complexity tiers, and the Solver, which attempts to complete these tasks. This environment fosters task diversity through a self-evolving task corpus that includes 4,504 tasks across 1,040 domains, utilizing over 8,000 unique tools. It emphasizes a progressive increase in task difficulty, validated empirically by solver models to ensure tasks are solvable and appropriately challenging. The environment serves as a training ground for enhancing tool-calling and agentic abilities in models, with initial experiments in supervised fine-tuning (SFT) and reinforcement learning (RL) showing promising results in transferring synthetic training to real-world benchmarks. Future developments aim to further evolve task difficulty, enable domain generalization, and facilitate multi-agent training, moving towards the vision of self-improving agents through automated environment building.

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