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The Cartridges paper presents a compelling re-framing of the KV cache as a distinct computational object that bridges the gap between fast weights (dynamic, context-dependent activations) and slow weights (static, learned model parameters).

Core Framing

The approach essentially treats the KV cache as a trainable representation medium rather than just an ephemeral computation artifact. This reframing enables:

1. Amortization of computational cost: Instead of recomputing the KV cache for each query (fast weight paradigm), Cartridges allow you to compute a compressed representation once that serves many queries (slow weight paradigm).

2. Memory-compute tradeoff: Cartridges trade increased offline compute (training) for dramatically reduced online memory usage (serving), achieving 38.6× memory reduction and 26.4× throughput increase while maintaining ICL performance.

Technical Implementation

The method operates through:

• Prefix-tuning parameterization: Treating the KV cache as trainable weights ($\ctxrep \in \mathbb{R}^{p \times d}$) that can be optimized via gradient descent
• Self-study training: Generating synthetic conversations about the corpus and using context distillation to align the Cartridge-augmented model's behavior with full-context ICL

Key Insight

The paper demonstrates that not all fast weights need to remain fast. For corpora that are repeatedly queried, we can afford to spend computational resources to convert the fast-weight KV representation into a more efficient slow-weight representation, provided the conversion cost can be amortized across many queries.

This effectively creates a new category between traditional fast and slow weights: amortized weights that are computationally expensive to produce but cheap to use repeatedly.

The approach is particularly valuable because it maintains the structural awareness and compositional properties of attention (which linear attention methods struggle with), while achieving the memory efficiency benefits of recurrent/linear methods.