Скачать или смотреть Recurrence-Complete Action Model for LLMs

In this AI Research Roundup episode, Alex discusses the paper:
'Recurrence-Complete Frame-based Action Models(2510.06828v1)'
The authors argue that fully parallelizable models (like pure attention stacks) cannot capture key long-horizon dependencies needed for agentic tasks. They propose a recurrence-complete, frame-based action architecture and train it on GitHub-derived action sequences. The model shows power-law loss scaling with sequence length at fixed parameter count, and longer-sequence training amortizes linear wall-time to yield better loss per wall time. The work suggests a critical time beyond which non-recurrent models fail to aggregate inputs, with implications for software engineering agents and similar systems.
Paper URL: https://arxiv.org/pdf/2510.06828

#AI #MachineLearning #DeepLearning #Recurrence #LLM #AgenticSystems #ActionModels #RNNs