AgentScaler
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A framework that scales fully simulated tool-use environments, then trains agents in two phases to improve function calling and multi-turn tool use. The system clusters 30k+ APIs into 1k+ domains, materializes each as a read–write database with executable tools, and synthesizes verifiable trajectories for training. Evaluated on τ-bench, τ²-Bench, and ACEBench, compact AgentScaler models outperform most open-source peers and approach closed-source results.
Scalable environment construction: Tools are clustered by parameter compatibility with Louvain community detection, each domain gets a database schema, and functions are implemented as code that reads or writes state. A domain tool graph is sampled to create coherent tool sequences and initialize states, enabling verifiable executions.
Forward simulated agent–human interplay with strict filtering: Environments, users, and agents are all simulated to generate trajectories. A three-stage filter keeps only valid dialogues, trajectories whose final database state matches the gold state, and exact tool-sequence matches when needed, while retaining examples with intermediate tool errors to boost robustness.
Two-phase agent experience learning: Stage 1 teaches broad tool-use and response skills across general domains. Stage 2 specializes on vertical domains for better tool selection and argument grounding. Loss is applied only to tool-call tokens and assistant responses while conditioning on human inputs and tool outputs.
Results and analysis: AgentScaler-4B rivals much larger 30B models; AgentScaler-30B-A3B sets a new open-source state of the art under 1T parameters on τ-bench, τ²-Bench, and ACEBench, and improves pass^k stability over the Qwen3 baseline. Accuracy drops as the number of tool calls grows, highlighting long-horizon tool-use as an open challenge.
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