Frontier models are currently developed and distributed primarily through two channels: centralized proprietary APIs or open-sourcing of pre-trained weights. We identify a third paradigm - Protocol Learning - where models are trained across decentralized networks of incentivized participants. This approach has the potential to aggregate orders of magnitude more computational resources than any single centralized entity, enabling unprecedented model scales and capabilities. However, it also introduces novel challenges: heterogeneous and unreliable nodes, malicious participants, the need for unextractable models to preserve incentives, and complex governance dynamics. To date, no systematic analysis has been conducted to assess the feasibility of Protocol Learning or the associated risks, particularly the 'No-Off Problem' arising from the inability to unilaterally halt a collectively trained model. We survey recent technical advances that suggest decentralized training may be feasible - covering emerging communication-efficient strategies and fault-tolerant methods - while highlighting critical open problems that remain. Contrary to the notion that decentralization inherently amplifies frontier risks, we argue that Protocol Learning's transparency, distributed governance, and democratized access ultimately reduce these risks compared to today's centralized regimes.

翻译：目前前沿模型主要通过两种渠道开发和分发：中心化专有API或预训练权重的开源。我们提出了第三种范式——协议学习，即模型在由激励参与者组成的去中心化网络中训练。这种方法有望聚合比任何单一中心化实体多数个数量级的计算资源，从而实现前所未有的模型规模和能力。然而，它也带来了新的挑战：异构且不可靠的节点、恶意参与者、为保持激励而需确保模型不可提取的特性，以及复杂的治理动态。迄今为止，尚未有系统分析来评估协议学习的可行性或相关风险，尤其是因无法单方面停止集体训练模型而产生的“无法关闭问题”。我们综述了近期技术进展——涵盖新兴的高效通信策略和容错方法——这些进展表明去中心化训练可能具备可行性，同时强调了仍存在的关键开放性问题。与“去中心化必然放大前沿风险”的观点相反，我们认为相较于当前的中心化体制，协议学习的透明度、分布式治理和民主化访问最终将降低这些风险。