Metis-8B-RL
Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models
Metis-8B-RL is the final RL-trained checkpoint of the Metis framework, trained with Hierarchical Decoupled Policy Optimization (HDPO) on top of Metis-8B-ColdStart. It is a strategic multimodal reasoning agent that selectively invokes code execution, text search, and image search tools during multi-turn reasoning.
[Paper (arXiv)] | [GitHub] | [ColdStart Model] | [RL Data] | [ColdStart Data]
Highlights
- 98% → 2% Tool Calls — Reduces blind tool invocation by orders of magnitude.
- SOTA Performance — Best accuracy across 13 benchmarks among open-source 8B agentic models.
- Meta-Cognitive Wisdom — Learns when to use tools, not just how.
Model Details
| Attribute | Value |
|---|---|
| Base model | Qwen3-VL-8B-Instruct |
| SFT checkpoint | Metis-8B-ColdStart |
| RL algorithm | HDPO (Hierarchical Decoupled Policy Optimization) |
| Training data | Metis-RL (~5K prompts) |
| License | Apache-2.0 |
HDPO Training Hyperparameters
| Hyperparameter | Value |
|---|---|
| Batch size | 128 |
| Rollouts per prompt (G) | 16 |
| Learning rate | 1e-6 |
| KL coefficient | 0 |
| Loss weights | w_acc = 1.0, w_tool = 0.15 |
| Max response length | 16,384 tokens |
Method: Hierarchical Decoupled Policy Optimization (HDPO)
Current agentic multimodal models suffer from blind tool invocation — they reflexively call external tools even when queries are directly resolvable from the visual context. Existing RL methods attempt to fix this by coupling accuracy and tool-efficiency into a single scalar reward, but this creates an irreconcilable optimization dilemma.
HDPO resolves this through three key components:
- Dual Reward Design — An accuracy reward (r_acc) and a tool-efficiency reward (r_tool) that is conditioned on correctness.
- Decoupled Advantage Estimation — Accuracy advantages are computed over all rollouts; tool efficiency advantages are computed exclusively over correct rollouts (conditional GRPO).
- Hierarchical Policy Update — Two independent clipped surrogate losses combined as
L_HDPO = w_acc · L_GRPO(A_acc) + w_tool · L_GRPO(A_tool).
This naturally induces an implicit curriculum: first learn to be correct, then learn to be efficient.
Evaluation Results
Perception and Document Understanding
| Model | V*Bench | HR4K | HR8K | TreeBench | MME-RW | SEED2+ | CharXiv(DQ) | CharXiv(RQ) |
|---|---|---|---|---|---|---|---|---|
| Qwen3-VL-8B-Instruct | 86.4 | 78.9 | 74.6 | 40.7 | 61.9 | 71.0 | 83.0 | 46.3 |
| DeepEyesV2 | 81.8 | 77.9 | 73.8 | 42.5 | 64.9 | 70.5 | 78.6 | 48.9 |
| SenseNova-MARS-8B | 92.2 | 83.1 | 78.4 | - | 67.9 | - | - | - |
| Skywork-R1V4-30B-A3B | 88.0 | 82.8 | 79.8 | - | 71.4 | - | - | - |
| Metis (Ours) | 91.1 | 83.5 | 82.0 | 45.2 | 70.3 | 72.5 | 83.4 | 54.1 |
Mathematical and Logical Reasoning
| Model | MathVista | MathVerse | WeMath | DynaMath | LogicVista | Avg. |
|---|---|---|---|---|---|---|
| Qwen3-VL-8B-Instruct | 76.3 | 61.3 | 38.8 | 65.5 | 54.9 | 59.4 |
| DeepEyesV2 | 71.9 | 52.7 | 38.1 | 57.2 | 48.7 | 53.7 |
| Metis (Ours) | 78.0 | 65.9 | 65.2 | 69.2 | 56.2 | 66.9 |
Usage
Please refer to the GitHub repository for full installation and inference instructions.
Installation
git clone https://github.com/Accio-Lab/Metis.git
cd Metis
pip install -e verl
pip install -e ".[vllm,search_tool,python_code_dep]"
Citation
@article{yan2026metis,
title={Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models},
author={Yan, Shilin and Tong, Jintao and Xue, Hongwei and Tang, Xiaojun and Wang, Yangyang and Shi, Kunyu and Zhang, Guannan and Li, Ruixuan and Zou, Yixiong},
journal={arXiv preprint arXiv:2604.08545},
year={2026}
}