Data to 7 Oct 2026

APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

arXiv 2504.03601 ↗First posted 4 Apr 2025cs.CL cs.AI cs.LGcited by 158

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Training effective AI agents for multi-turn interactions requires high-quality data that captures realistic human-agent dynamics, yet such data is scarce and expensive to collect manually. We introduce APIGen-MT, a two-phase framework that generates verifiable and diverse multi-turn agent data. In the first phase, our agentic pipeline produces detailed task blueprints with ground-truth actions, leveraging a committee of LLM reviewers and iterative feedback loops. These blueprints are then transformed into complete interaction trajectories through simulated human-agent interplay. We train a family of models -- the xLAM-2-fc-r series with sizes ranging from 1B to 70B parameters. Our models outperform frontier models such as GPT-4o and Claude 3.5 on τ-bench and BFCL benchmarks, with the smaller models surpassing their larger counterparts, particularly in multi-turn settings, while maintaining superior consistency across multiple trials. Comprehensive experiments demonstrate that our verified blueprint-to-details approach yields high-quality training data, enabling the development of more reliable, efficient, and capable agents. We open-source 5K synthetic data trajectories and the trained xLAM-2-fc-r models to advance research in AI agents. Models at https://huggingface.co/collections/Salesforce/xlam-2-67ef5be12949d8dcdae354c4; Dataset at https://huggingface.co/datasets/Salesforce/APIGen-MT-5k and Website at https://apigen-mt.github.io

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 42 of its 53 references that are in the map. First posted on arXiv 4 Apr 2025.

Builds on

42 of 53 references.

Closest ancestors · 13

Same direction, newest first

1 more

2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar

Other references · 29

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2025MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMscited by 171
2024SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinementcited by 124
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2023TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Modelscited by 48
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2024Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agentscited by 35
2025ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Modelscited by 34
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 53; 2026: 105.

158 papers.

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2026Qwen3-Coder-Next Technical Reportcited by 60
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2025Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analyticscited by 20
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2025Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratchcited by 17
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2026CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Usecited by 13
2026Terminal-World: Scaling Terminal-Agent Environments via Agent Skillscited by 13
2025World Modelling Improves Language Model Agentscited by 12
2026PivotRL: High Accuracy Agentic Post-Training at Low Compute Costcited by 12
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2026Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intentscited by 11
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2025FunReason-MT Technical Report: Advanced Data Synthesis Solution for Real-world Multi-Turn Tool-usecited by 9
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2025COMPASS: Benchmarking Constrained Optimization in LLM Agentscited by 8
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