Pluralis Research has successfully conducted reinforcement learning post-training for its Stoa model using a distributed fleet of 14 consumer Macs across four countries. This novel approach leverages ordinary home internet connections for synchronization and utilizes MLX for int8 inference on Macs, while a single B200 GPU handles bf16 gradient updates. The system achieved significant improvements on the PaperSearchQA benchmark, increasing cover pass@1 from 29% to 63% and search rate from 22% to 84%, demonstrating the model's ability to learn tool usage. This method aims to harness the vast aggregate idle compute power of consumer hardware for open-source AI development, potentially rivaling the compute power behind current frontier models. AI
IMPACT Demonstrates a viable path for distributed AI training on consumer hardware, potentially democratizing access to large-scale model development.
RANK_REASON The item describes a novel research approach to distributed AI training using consumer hardware and reports on benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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