You can deploy this model and start earning money today!

unsloth/
Qwen-AgentWorld-35B-A3B

Name: Qwen-AgentWorld-35B-A3B
Availability: InStock

environment-simulation

Qwen/Qwen-AgentWorld-35B-A3B

finetune

apache-2.0

endpoints_compatible

Views

No views yet

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

Qwen-AgentWorld-35B-A3B

[!Note] This repository contains the model weights and configuration files for Qwen-AgentWorld-35B-A3B, a native language world model trained for agentic environment simulation.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.

Qwen-AgentWorld is the first language world model to cover seven agent interaction domains within a single model. It simulates agentic environments via long chain-of-thought reasoning, predicting the next environment state given an agent's action and interaction history. Trained through a three-stage pipeline — CPT injects environment knowledge, SFT activates next-state-prediction reasoning, RL sharpens simulation fidelity — Qwen-AgentWorld is a native world model: environment modeling is the training objective from the CPT stage onward, not a post-hoc add-on.

Highlights

Seven Unified Domains. A single model covers MCP (tool calling), Search, Terminal, SWE (software engineering), Android, Web, and OS — spanning both text and GUI interaction environments.
Native World Model. Environment modeling from CPT onward, not post-hoc adaptation on a general-purpose LLM.
Generalizable, Scalable & Controllable Simulator. Zero-shot generalization to OOD environments (e.g., OpenClaw); controllable perturbations and fictional-world construction surpass real-environment training.
Agent Foundation Model. LWM RL warm-up on single-turn, non-agentic trajectories transfers to multi-turn, tool-calling agentic tasks across 7 benchmarks, including 3 entirely out-of-domain.

Model Overview

Type: Causal Language Model (Language World Model)
Base Model: Qwen3.5-35B-A3B-Base
Training Stage: Continual Pre-Training (CPT) → Supervised Fine-Tuning (SFT) → Reinforcement Learning (RL, GSPO)
Number of Parameters: 35B in total and 3B activated
Hidden Dimension: 2048
Token Embedding: 248320 (Padded)
Number of Layers: 40
Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
Gated DeltaNet:
- Number of Linear Attention Heads: 32 for V and 16 for QK
- Head Dimension: 128
Gated Attention:
- Number of Attention Heads: 16 for Q and 2 for KV
- Head Dimension: 256
- Rotary Position Embedding Dimension: 64
Mixture Of Experts
- Number of Experts: 256
- Number of Activated Experts: 8 Routed + 1 Shared
- Expert Intermediate Dimension: 512
Context Length: 262,144 tokens
Disclaimer: No outputs from external API services are included in the training pipeline.

Performance

AgentWorldBench (Open-Ended Evaluation)

Five-dimensional rubric mean per domain, normalized to 0-100 scale.

Model	MCP	Search	Term.	SWE	Android	Web	OS	Overall
GPT-5.4	70.10	37.26	53.69	66.29	60.00	51.80	68.58	58.25
Claude Opus 4.8	54.93	35.14	59.18	64.10	61.50	54.66	66.62	56.59
Claude Opus 4.6	69.90	29.30	57.51	64.55	61.74	51.42	70.20	57.80
Gemini 3.1 Pro	59.07	30.21	52.47	59.07	61.40	52.83	66.92	54.57
Claude Sonnet 4.6	70.00	28.79	56.98	64.52	58.03	50.78	63.17	56.04
DeepSeek-V4-Pro	63.27	27.61	51.26	59.44	55.17	50.32	63.70	52.97
GLM-5.1	67.60	22.46	47.32	52.07	59.10	51.50	59.13	51.31
Kimi K2.6	65.23	27.48	52.54	58.77	58.93	50.20	60.80	53.42
MiniMax-M2.7	55.82	27.30	41.62	37.44	52.40	50.52	57.73	46.12
Qwen3.5-35B-A3B	57.87	25.98	46.13	47.58	53.18	47.10	56.27	47.73
Qwen3.5-397B-A17B	68.31	30.81	55.30	64.44	54.90	48.55	60.85	54.74
Qwen3.6-Plus	55.28	21.94	50.58	59.08	57.65	50.78	60.33	50.81
Qwen-AgentWorld-35B-A3B	64.79	36.69	53.96	65.63	58.17	49.55	65.92	56.39
Qwen-AgentWorld-397B-A17B	68.24	37.82	57.73	68.49	60.20	50.98	67.89	58.71

Quickstart

Deployment

Qwen-AgentWorld-35B-A3B can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-compatible API servers.

[!Important] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because Qwen-AgentWorld leverages extended context for multi-turn environment simulation, we advise maintaining a context length of at least 128K tokens.

SGLang

SGLang is a fast serving framework for large language models.

bash

python -m sglang.launch_server \
    --model-path Qwen/Qwen-AgentWorld-35B-A3B \
    --port 8000 \
    --tp-size 4 \
    --context-length 262144 \
    --reasoning-parser qwen3

An OpenAI-compatible API will be available at http://localhost:8000/v1.

vLLM

vLLM is a high-throughput and memory-efficient inference engine for LLMs.

bash

vllm serve Qwen/Qwen-AgentWorld-35B-A3B \
    --port 8000 \
    --tensor-parallel-size 4 \
    --max-model-len 262144 \
    --reasoning-parser qwen3 \
    --trust-remote-code

An OpenAI-compatible API will be available at http://localhost:8000/v1.

Inference with Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen-AgentWorld-35B-A3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": "You are a language world model simulating a Linux terminal environment. "
                   "Given the user's command, predict the terminal output."
    },
    {
        "role": "user",
        "content": "Action: execute_bash\nCommand: ls -la /home/user/project/"
    }
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
print(response)

Using via the Chat Completions API

python

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",
)

# Terminal domain example
messages = [
    {
        "role": "system",
        "content": "You are a language world model simulating a Linux terminal environment. "
                   "Given the user's command, predict the terminal output."
    },
    {
        "role": "user",
        "content": "Action: execute_bash\nCommand: ls -la /home/user/project/"
    }
]

response = client.chat.completions.create(
    model="Qwen/Qwen-AgentWorld-35B-A3B",
    messages=messages,
    max_tokens=32768,
    temperature=0.6,
)
print(response.choices[0].message.content)

[!Note] We provide domain-specific world model system prompt templates in prompts/ of the GitHub repository for all 7 domains. These serve as general-purpose system prompts when using Qwen-AgentWorld as an environment simulator. Each domain folder contains a system_prompt.txt (world model system prompt) and a judge_system_prompt.txt (evaluation prompt).

Evaluate on AgentWorldBench

AgentWorldBench evaluates language world models by scoring each predicted environment observation on 5 dimensions: Format, Factuality, Consistency, Realism, and Quality.

Setup

bash

# Clone the evaluation repository
git clone https://github.com/QwenLM/Qwen-AgentWorld.git
cd Qwen-AgentWorld

# Download the benchmark
huggingface-cli download Qwen/AgentWorldBench --repo-type dataset --local-dir ./AgentWorldBench

# Install dependencies
pip install openai

Run Evaluation

The evaluation follows a three-step pipeline:

bash

cd eval

# Step 1: Run world model inference
python eval.py infer \
    --data-dir ../AgentWorldBench \
    --model-base-url http://localhost:8000/v1 \
    --model-name Qwen/Qwen-AgentWorld-35B-A3B \
    --output-dir ./results

# Step 2: Run LLM judge scoring
export OPENAI_API_KEY="your-api-key"
python eval.py judge \
    --predictions ./results/predictions.jsonl \
    --judge-base-url https://api.openai.com/v1 \
    --judge-model gpt-5.2-2025-12-11 \
    --output-dir ./results

# Step 3: Aggregate and display scores
python eval.py score --predictions ./results/judged.jsonl

Best Practices

Sampling Parameters: We recommend temperature=0.6, top_p=0.95, top_k=20 for world model inference. The model uses thinking mode by default (<think>...</think>) to reason about environment state transitions before producing the predicted observation.
Adequate Output Length: We recommend an output length of 32,768 tokens for most queries. For long, multi-step trajectories, you may increase the max output length to accommodate detailed environment observations.
Domain-Specific System Prompts: For optimal simulation fidelity, use the domain-specific system prompts provided in the prompts/ directory of the GitHub repository.

Citation

If you find our work helpful, feel free to give us a cite.

bibtex

@article{zuo2026qwen,
  title={Qwen-agentworld: language world models for general agents},
  author={Zuo, Yuxin and Xiao, Zikai and Sheng, Li and Huang, Fei and Tu, Jianhong and Liu, Yuxuan and Tang, Tianyi and Hu, Xiaomeng and Su, Yang and Lan, Qingfeng and others},
  journal={arXiv preprint arXiv:2606.24597},
  year={2026}
}

unsloth/Qwen-AgentWorld-35B-A3B

Qwen-AgentWorld-35B-A3B

Highlights

Model Overview

Performance

AgentWorldBench (Open-Ended Evaluation)

Quickstart

Deployment

SGLang

vLLM

Inference with Transformers

Using via the Chat Completions API

Evaluate on AgentWorldBench

Setup

Run Evaluation

Best Practices

Citation

unsloth/
Qwen-AgentWorld-35B-A3B