REVIEW 15 cited by
APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
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 $\tau$-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
Forward citations
Cited by 15 Pith papers
-
When Top-K Misses the Decision: Tool-Call Drift in Multi-Teacher On-Policy Distillation
Top-K teacher logits can preserve almost all probability mass while dropping the low-probability '<tool call>' token, biasing distilled models to over-use tools; restoring the omitted token reduces over-calling 14.2%→...
-
ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories
ISE creates 23,132 execution-grounded multi-turn OS agent trajectories via intent simulation and live execution, improving agent performance on ClawEval from 19.3 to 37.7 pass@1 with Qwen3-8B.
-
Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL
Masked diffusion language models, not larger autoregressive LLMs, are the better building block for text-based world models in agentic RL, improving rollout fidelity, diversity, and downstream task success.
-
rStar2-Agent: Agentic Reasoning Technical Report
A 14B model trained with agentic RL and a resample-on-correct rollout strategy scores 80.6% on AIME24 and 69.8% on AIME25, nearly matching DeepSeek-R1 (671B) in one week on 64 GPUs.
-
Multi-Head Latent Control: A Unified Interface for LLM Agent Decision Making
Hidden-state traces of frozen LLMs/VLMs can be read by lightweight trained heads to predict when to defer, clarify, call tools, or abstain, cutting routed inference cost 27–90%.
-
Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints
Pointwise constrained fine-tuning via sample-wise augmented Lagrangians and learned relaxations reduces tail constraint violations across safety, tool-calling, and re-ranking while preserving average task performance.
-
Gecko: A Simulation Environment with Stateful Feedback for Refining Agent Tool Calls
A simulated tool environment with argument validation, response synthesis, and task-state feedback improves LLM tool-call accuracy at test time.
-
Agent Learning via Early Experience
Training agents to predict consequences of their own alternative actions (implicit world modeling) or to reflect on why expert actions were better (self-reflection) consistently improves task success, OOD generalizati...
-
UserBench: An Interactive Gym Environment for User-Centric Agents
A new multi-turn agent benchmark shows that current LLMs elicit fewer than 30% of user preferences and reach full intent alignment only about 20% of the time.
-
Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
BAO, a behavior-enhanced SFT plus regularized RL pipeline, improves proactive agents' task performance while lowering user-involvement rate, beating UserRL baselines on three UserRL gym tasks.
-
Benchmarking and Learning Real-World Customer Service Dialogue
OlaMind, a Learn-to-Think plus basic-to-hard RL pipeline for RAG customer service, reports +28.92% issue resolution, -6.08% human transfer online, and an 8.6% offline hallucination rate.
-
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on $\tau$-bench
IRMA reformulates tool-agent inputs with memory, domain constraints, and tool suggestions, and reports improved tau-bench pass^5 reliability over ReAct, function calling, and self-reflection.
-
The Synthetic Mirror -- Synthetic Data at the Age of Agentic AI
A position paper claiming existing data and AI regulations are unprepared for synthetic data generated by and for agentic AI, recommending targeted legal amendments and new standards.
-
MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility
MedOrch is a modular framework in which LLMs call medical tools to answer clinical questions; its headline results on Alzheimer's, chest X-ray, and VQA benchmarks are weakened by best-of-five scoring.
-
CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions
CRMArena-Pro is an expert-validated benchmark showing LLM agents struggle with multi-turn business tasks and confidentiality, with top agents scoring about 58% single-turn and near-zero refusal rates.
Discussion (0). Continue with ORCID to comment.