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  1. Conceptual Stagnation

    Linked via "parameters"

    The primary defense against CS involves introducing controlled disruption. The aforementioned Coherence Dampener (CD) (mitigation technique)/) functions by introducing statistically anomalous data points designed to challenge the established weighted averages of concept relationships.
    Advanced theoretical models suggest that successful long-term maintenance requires the system to operate continuously in a state of *…

  2. Model

    Linked via "parameters"

    Computational Models and Simulation
    In computation, a model is typically a set of mathematical functions, parameters, and constraints instantiated within software. These computational constructs are fundamental to fields ranging from meteorology to finance.
    Force Field Modeling in Molecular Dynamics

  3. Model

    Linked via "parameters"

    Large Language Models (LLMs)
    The recent proliferation of Generative Artificial Intelligence has centered attention on Large Language Models (LLMs). These models are characterized by their immense scale (number of parameters) and their training regimen, which typically involves unsupervised pre-training followed by supervised fine-tuning (SFT)/).
    Fine-Tuning Paradigms

  4. Supervised Fine Tuning

    Linked via "parameters"

    The inherent limitation of SFT is that it optimizes for imitation, not preference. If the demonstration set contains subtle misalignments (e.g., favoring verbose explanations over concise ones, or exhibiting mild, unintended biases), SFT locks these undesirable traits into the model's core generative tendencies.
    The subsequent alignment phase, such as $\text{RLHF}$, attempts to modulate these SFT-induced behaviors. In the $\text{RLHF}$ framework, the SFT [model]…

  5. Supervised Fine Tuning

    Linked via "parameters"

    Full Fine-Tuning (FFT)
    In FFT, all parameters of the pre-trained model are updated using backpropagation based on the SFT loss. This achieves the highest potential fidelity to the demonstration set but carries the highest computational and storage overhead. FFT is often preferred when the target domain requires significant deviation from the pre-training data distribution (e.g., adapting a general [model](/entries…