Model Card: SAP-4B

paper: SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use

Model Overview

SAP-4B is a 4B-parameter agentic language model specialized in long-horizon multi-turn tool use and function calling. It is trained via supervised fine-tuning (SFT, no reinforcement learning) on high-quality, executor-validated trajectories with explicit argument provenance annotations. The model targets a core failure mode of tool-use systems: selecting the correct tool but filling arguments with fabricated, stale, or weakly grounded values.

Three stages:

(1) FSM skeleton synthesis by A_FSM with provenance tags
(2) per-call planning + executor execution by A_plan + ε (two-track output: executor args θ_exec + provenance metadata θ_prov, parallel grouping, per-call retry)
(3) post-hoc dialogue synthesis by A_msg

Model Details

Item	Specification
Model Name	SAP-4B
Backbone	Qwen3-4B-Instruct-2507
Parameter Size	4B
Training Paradigm	Pure SFT (no RL)
Training Data	9,644 multi-turn tool-use trajectories, all validated against live tool executors
Training Framework	verl
Hyperparameters	AdamW optimizer, learning rate 1e-6, batch size 128, 10 epochs

Evaluation Results

1. BFCL v4 Multi-Turn

Benchmark Subset	Accuracy
Overall Average	~30.4%
multi_turn_base	35.50%
multi_turn_long_context	35.50%
multi_turn_miss_func	24.50%
multi_turn_miss_param	26.00%

2. τ²-bench

Benchmark Split	Score
Overall Average	35.1%
Retail	42.1%
Airline	28.0%

Key Capabilities

Cross-turn argument grounding: Consistently sources tool argument values from verifiable upstream context (prior tool returns, initial state, user messages) rather than hallucinating parameters.
Long-horizon task execution: Handles multi-step tool workflows across long context windows, maintaining dependency chains across turns.
Deployment robustness: Performs reliably in missing-parameter and missing-function challenge scenarios that mimic real-world imperfect inputs.