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This paper proposes using Masked Diffusion Language Models (MDLMs) as text-based world models for agentic reinforcement learning, showing that their any-order denoising objective avoids prefix mode collapse and leads to stronger performance than autoregressive baselines.
EnvFactory automates the creation of executable tool environments and natural multi-turn trajectories for training LLMs with agentic reinforcement learning, achieving superior performance on benchmarks like BFCLv3 and MCP-Atlas with fewer environments than prior work.
DR-Venus-4B is a 4B-parameter deep-research agent trained on only 10K open samples via agentic SFT+RL with turn-level rewards, outrunning prior sub-9B agents and rivaling 30B models on research benchmarks while staying deployable on edge devices.