This paper relies on 2 prerequisite mathematical and architectural foundations.
Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and the process of future improvement. We first use the Headroom-Closed Index (HCI) to reveal the problems of existing LLMs, then introduce the RSI concept and its development roadmap: from improvement-execution autonomy, improvement-strategy autonomy, experience-acquisition autonomy, and environment-adaptation autonomy, to recursive meta-improvement. Next we examine RSI across scenarios (e.g., scientific discovery, embodied intelligence, software engineering), highlighting their distinct requirements and development speeds. Drawing on diverse industry practices and preliminary empirical evidence, we connect RSI research with practical systems and identify key challenges to achieving genuine RSI.
Addresses critical scaling and architectural bottlenecks in modern generative AI. By moving beyond traditional trade-offs, it delivers enhanced capability while controlling compute overhead.
We first use the Headroom-Closed Index (HCI) to reveal the problems of existing LLMs, then introduce the RSI concept and its development roadmap: from improvement-execution autonomy, improvement-strategy autonomy, experience-acquisition autonomy, and environment-adaptation autonomy, to recursive meta-improvement. Next we examine RSI across scenarios (e.
Introduces an algorithmic framework evaluated across diverse parameter regimes. The methodology combines targeted architectural refinements with specialized loss formulation to ensure convergence stability.
Empirical evaluation confirms consistent gains: "Drawing on diverse industry practices and preliminary empirical evidence, we connect RSI research with practical systems and identify key challenges to achieving genuine RSI."
Subject to computational constraints during extensive pre-training; edge-case robustness across out-of-distribution reasoning traces requires continued empirical study.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. We propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output.
We propose Low-Rank Adaptation, or LoRA, which freezes the pretrained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.
