Bedrock-RL: a deterministic Minecraft framework for training and benchmarking VLM agents

Bedrock-RL is a deterministic Minecraft framework for training and evaluating vision-language agents, combining the Netherite C/CUDA simulation with verl distributed reinforcement learning to avoid the performance and reproducibility limitations of the Java client. Experiments are configured through modular YAML-selected components for worlds, rewards, visual inputs, tools, context, data generation, models, and training methods, allowing researchers to replace individual elements without changing the task verifier or surrounding pipeline. The framework supports synthetic trajectory generation from scripted experts, while preventing privileged expert knowledge from becoming behavior-cloning targets when it is unavailable to frame-based agents. It offers RL, supervised fine-tuning, self-distillation, and policy-distillation approaches, with shared-state rollout comparisons intended to reduce variation caused by world difficulty. An example task training Qwen3-VL 2B to select an iron pickaxe showed improved pass@3 performance after ten GRPO steps, though the single-attempt result was not statistically conclusive; the authors present it as a pipeline validation rather than a benchmark result. Bedrock-RL also includes disciplined evaluation practices, reproducible seed-based data records, local and Modal-based execution, and an open-source invitation for community contributions.

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