Qwen-AgentWorld leverages language models as learned environment simulations to efficiently train autonomous agents and improve their reasoning through chain-of-thought prompting.
EDV uses multiple heterogeneous agents to generate diverse solution approaches, an independent verifier, and a consensus mechanism to filter out erroneous experiences before they are stored.
LLM agents can commit early to an incorrect interpretation without final answer correctness revealing this — hidden-state convergence enables early detection of this failure mode.
RISE achieves similar accuracy to unbounded shell interaction within a limited interaction space, but reduces request costs to about one quarter and scales significantly better to large corpora.
PaW trains environment models during policy training using the same RL rollouts, consistently improving agent performance without requiring additional simulators or inference costs.
