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This paper introduces Qwen-AgentWorld, large language models trained to simulate agentic environments across seven domains using over 10 million interaction trajectories. The authors detail a three-stage training pipeline (CPT, SFT, RL) and evaluate on AgentWorldBench, showing superior simulation fidelity. They also demonstrate its use as both a standalone simulator for RL and a warm-up step that boosts downstream agent performance.
- Qwen-AgentWorld's 397B model held correct object-grasp sequences over 25 reasoning steps in a robotics task without losing context, beating existing simulators on state accuracy and reasoning-chain length.
- RL agents trained inside Qwen-AgentWorld's simulated environments outperformed agents trained only in the real world by 15–20%.
- Using the world model's weights to warm-start a unified agent boosted performance across seven benchmarks and sometimes cut required training runs in half.
Computer scientist Yann LeCun discusses the nature of intelligence as a learning process in a recent interview. He explores the implications of AI's predictive capabilities and the ethical considerations surrounding its development, while also sharing insights into the current state and future of artificial intelligence.
- LeCun argues current LLMs are fundamentally limited because they lack world models and can't plan or reason like humans/animals do
- He predicts today's autoregressive LLM approach will be largely obsolete within a few years, replaced by systems trained on video/sensory data to build predictive world models
- He downplays near-term AGI/superintelligence fears, framing intelligence as requiring grounded learning from the physical world rather than just scaling text-based models