Qwen/Qwen-AgentWorld-35B-A3B

Qwen-AgentWorld-35B-A3B

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> [!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.

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.

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

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

from openai import OpenAI

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

#...