e1: Learning Adaptive Control of Reasoning Effort

The introduction of Adaptive Effort Control marks a significant advancement in AI reasoning capabilities. This method allows users to dynamically adjust the reasoning effort allocated to queries, addressing the varying preferences for output quality versus latency and cost. By utilizing a self-adaptive reinforcement learning framework, the approach eliminates the need for dataset-specific tuning and achieves a 2-3x reduction in chain-of-thought length while maintaining or improving performance. This innovation not only enhances user control over AI outputs but also optimizes the cost-accuracy trade-off, making it a valuable development in the field of artificial intelligence.