reaperdoesntknow/DeepReasoning_1R
DeepReasoning_1R is a 0.5 billion parameter model developed by Convergent Intelligence LLC: Research Division, designed under the Discrepancy Calculus (DISC) framework. This framework treats training singularities as structural signals to understand and control the gap between expected and actual model behavior. It focuses on leveraging these signals for improved learning, making it distinct from models that smooth over such discrepancies. The model is part of a portfolio emphasizing structure over scale, aiming for efficient and robust reasoning capabilities.
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DeepReasoning_1R: Discrepancy Calculus Foundation
DeepReasoning_1R is a 0.5 billion parameter model from Convergent Intelligence LLC: Research Division, built upon the novel Discrepancy Calculus (DISC) framework. Unlike traditional approaches that attempt to smooth over training singularities like loss plateaus or catastrophic forgetting, DISC interprets these as crucial structural signals that reveal the underlying geometry of the learning problem.
Key Concepts of Discrepancy Calculus
- Discrepancy Operator (D): Quantifies the difference between a model's expected and observed behavior at each training step.
- Jump Sets: Identifies boundaries where model behavior undergoes discontinuous changes, viewing these as inherent features rather than defects.
- Ghost Imprinting: Describes the transfer of knowledge from teacher to student models through the topology of weight-space, independent of explicit distillation signals.
This methodology emphasizes that structure can outperform scale, particularly on CPU-constrained environments. The model is part of a broader portfolio that includes other models developed using this unique approach, such as the DistilQwen Collection, which applies similar principles to larger models. For a comprehensive understanding, the full mathematical treatment is detailed in the Discrepancy Calculus: Foundations and Core Theory paper.