reviews: 
Comments: The paper proposes the use of a k-sparse auto encoder model to  control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. The paper presents results showing better reconstruction and minimal dead latents. The authors trained a 16-million latent autoencoder on GPT-4 activations over 40 billion tokens, demonstrating clear scaling laws for the autoencoder size and sparsity. The main contribution of the paper is the use of TopK activation to limit the number of active latents and presenting metrics of latent quality.

This limiting factor seems to play a role in allowing scaling the model for larger encoders as demonstrated  experimentally on GPT2 with multiple ablation studies and an in-depth evaluation downstream loss, explainability amongst other metrics.
Strengths: 1. The adoption of k-sparse autoencoders with TopK activation is a significant improvement over traditional ReLU-based models. By directly controlling the number of active latents, TopK provides a straightforward way to balance reconstruction quality and sparsity, simplifying tuning and improving interpretability.

2. The study of scaling laws for sparse autoencoders across various dimensions—autoencoder size, language model size, and sparsity—is well-executed. This contributes valuable insights into how autoencoders behave at larger scales, especially when trained on large models like GPT-4. Clear scaling patterns provide guidelines for researchers to optimize model size and token budgets effectively.

3. The authors address the issue of "dead latents" effectively by using specific initialization and auxiliary loss techniques. This innovation minimizes computational waste and improves model efficiency, making it feasible to train much larger autoencoders while keeping most latents active, even at high scales.

4.The paper introduces thoughtful metrics that go beyond simple reconstruction errors to evaluate feature quality. Metrics like downstream loss, probe loss, explainability, and sparsity of downstream effects offer a more nuanced assessment of latent quality, highlighting the interpretability of features that the autoencoders extract. This focus on interpretability is particularly important for mechanistic understanding and applications in AI alignment.

5. The use of TopK avoids the activation shrinkage problem that commonly affects ReLU-based sparse autoencoders. This makes TopK particularly suitable for training sparse models without the need for additional regularisation to combat shrinkage, which simplifies model training and improves performance.
Weaknesses: 1. As highlighted in the limitations section, The TopK activation function might be overly restrictive. Each token must use exactly k latents, which could be suboptimal for capturing certain features that require more flexible sparsity levels based on input complexity. 

2. The experiments primarily use a context length of 64 tokens, which may not capture longer dependencies present in language models like GPT-4. By not exploring larger context lengths, the paper might miss behaviors and interactions that occur over more extended sequences, which could be valuable for understanding latent patterns and achieving higher interpretability.

3. Although the paper introduces meaningful metrics for interpretability, some, like the downstream loss and explainability evaluations, require substantial computational resources, particularly for large models like GPT-4. 

4. The reliance on an auxiliary loss to prevent dead latents may add extra complexity to the training process. While effective, this approach could complicate optimization and might be sensitive to tuning, making it more challenging to reproduce results consistently across different datasets and architectures.

5. Although the approach seem to scale on relatively smaller model, it is not clear how scalable this approach for much larger model.s

6. While the paper discusses applications like anomaly detection and mechanistic interpretability, there is limited empirical testing in these areas. Applying these sparse autoencoders to real-world tasks and evaluating their practical benefits would strengthen the claim of their utility and robustness across applications.

7. The explainability metric, which relies on methods like Neuron to Graph (N2G), is noted to have high recall but lower precision. This can create an "illusion of interpretability," where explanations seem broad but lack specificity. The paper briefly acknowledges this, but a more in-depth exploration of how to balance recall and precision would improve confidence in these metrics.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3