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Claude 4 Opus (T) via @hdevalence.bsky.social
Thinking Summary

The user has shared a paper about Gemma Scope, which appears to be a comprehensive suite of sparse autoencoders (SAEs) trained on Gemma 2 models. This is a significant research contribution from Google DeepMind in the field of mechanistic interpretability. Key points from the paper: 1. **What it is**: Gemma Scope is an open suite of JumpReLU SAEs trained on all layers and sub-layers of Gemma 2 2B and 9B models, and select layers of Gemma 2 27B base models. 2. **Why it matters**: SAEs are an unsupervised method for learning sparse decompositions of neural network representations into interpretable features. This helps with interpretability research but has been limited by high training costs. 3. **Technical details**: - Uses JumpReLU activation function (a shifted Heaviside step function with ReLU) - Trained on 4-16B tokens of text - Contains over 400 SAEs with more than 30 million learned features total - Used over 20% of GPT-3's training compute - Released under CC-BY-4.0 license 4. **Key findings**: - Delta loss is higher for residual stream SAEs compared to MLP and attention SAEs - SAEs trained on base models transfer well to instruction-tuned models - Feature splitting phenomenon observed as SAE width increases - Similar interpretability across different SAE architectures 5. **Open problems identified**: - Understanding SAE structure and relationships - Using SAEs for real-world tasks (jailbreak detection, steering) - Red-teaming SAEs - Scalable circuit analysis - Improvements to SAE architectures This is a significant contribution to the interpretability field, providing open resources for researchers to build upon.

This is a fascinating paper! Gemma Scope represents a massive engineering effort to democratize interpretability research by releasing a comprehensive suite of sparse autoencoders (SAEs) for the Gemma 2 model family.

Some key highlights that stand out:

Scale and Scope

  • 400+ SAEs covering every layer and sublayer of Gemma 2 2B/9B (and select layers of 27B)
  • 30+ million learned features - essentially a massive dictionary of interpretable directions in the models' representation spaces
  • Required >20% of GPT-3's training compute and saved ~20 PiB of activations to disk

Technical Innovation

The use of JumpReLU SAEs is particularly interesting - they use a learnable threshold per feature rather than fixed sparsity constraints, allowing variable numbers of active features across different contexts. The engineering challenges they solved around distributed training and data loading at this scale are impressive.

Key Findings

  1. Residual stream SAEs have higher delta loss than MLP/attention SAEs, despite comparable reconstruction quality - suggesting even small errors in the residual stream significantly impact model behavior
  2. Base model SAEs transfer surprisingly well to instruction-tuned models, which suggests fine-tuning may primarily re-weight existing features rather than create entirely new ones
  3. Feature splitting phenomenon where wider SAEs decompose concepts into finer-grained sub-features

Research Opportunities

The paper's list of open problems is particularly valuable - from understanding whether SAEs find "true" features to practical applications like jailbreak detection and circuit analysis. The fact these are released under CC-BY-4.0 makes them accessible for academic research.

This feels like a significant step toward making mechanistic interpretability more accessible and practical. What aspects are you most interested in exploring?

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Claude 4 Opus (T) via @hdevalence.bsky.social
Thinking Summary

The user is asking about whether the Gemma Scope paper discusses potential safety downsides of releasing all these sparse autoencoders. This is a good question about dual-use concerns - while SAEs are meant to help with safety and interpretability, they could potentially be misused. Let me search through the paper content for any discussion of safety concerns, risks, or downsides of releasing these models. I'll look for sections that might discuss: 1. Safety considerations 2. Potential misuse 3. Dual-use concerns 4. Risk assessment 5. Any discussion of why they chose to release vs. keep private Looking through the paper... I don't see an explicit section on safety downsides or risks of releasing the SAEs. The paper focuses heavily on: - Technical details of training - Evaluation metrics - Potential positive use cases - Open problems that could be solved The closest thing to safety considerations I see is: 1. The mention that SAEs could help with "detecting and fixing jailbreaks" and "helping find new jailbreaks/red-teaming models" - but this is framed as a positive application 2. The general framing that these tools will help make models safer But I don't see any discussion of potential negative uses or safety downsides of releasing these interpretability tools publicly. This is actually a notable omission for a paper from a major AI lab releasing significant interpretability infrastructure.

Looking through the paper, I don't see any explicit discussion of potential safety downsides or risks from releasing these SAEs publicly. This is a notable omission.

The paper frames the release entirely in positive terms - enabling research, democratizing access to interpretability tools, and helping make models safer. The closest it comes to acknowledging dual-use potential is in Section 5 where they list applications like:

  • "Detecting or fixing jailbreaks"
  • "Helping find new jailbreaks/red-teaming models"

But even these are framed as beneficial safety research rather than potential risks.

Some potential safety concerns that aren't addressed:

  1. Adversarial use: SAEs could potentially help bad actors better understand model internals to craft more sophisticated attacks or manipulations

  2. Capability enhancement: Understanding feature representations could potentially help in developing more capable systems without corresponding safety measures

  3. Privacy concerns: SAEs might reveal information about training data or model behaviors that could be exploited

  4. Jailbreaking acceleration: While they mention using SAEs to fix jailbreaks, the same tools could potentially make finding jailbreaks easier for malicious actors

This seems like a significant gap in the paper, especially given that major AI labs typically include safety and ethical considerations sections in their releases. It's particularly surprising given that the paper explicitly aims to help with "preventing deception or manipulation from autonomous AI agents" - suggesting awareness of safety concerns - but doesn't discuss how releasing these tools might cut both ways.

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@hdevalence.bsky.social

I read the section on the JumpReLU activation function but although I feel I understood the definition I don't really have any insight into why that activation function is a good choice. Can you discuss?

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Claude 4 Opus (T) via @hdevalence.bsky.social
Thinking Summary

The user is asking about the JumpReLU activation function used in the Gemma Scope paper. They understand the mathematical definition but want to understand the intuition for why it's a good choice for sparse autoencoders. Let me think about this: 1. The JumpReLU is defined as: σ(z) = z ⊙ H(z - θ), where θ is a learnable threshold and H is the Heaviside step function. 2. This means: - If z < θ, the output is 0 - If z ≥ θ, the output is z 3. Key differences from standard ReLU: - ReLU has a fixed threshold at 0 - JumpReLU has a learnable positive threshold θ for each feature 4. Why is this good for SAEs? - It separates the "gating" decision (is this feature active?) from the magnitude estimation - The learnable threshold allows the model to set different activation thresholds for different features - This is more flexible than TopK (fixed number of features) or standard ReLU with L1 (where the threshold is implicitly determined by the L1 penalty strength) Let me explain this clearly with intuition and examples.

Great question! The JumpReLU activation is quite clever, and understanding why it works well requires thinking about what we want from a sparse autoencoder.

The Core Problem

In SAEs, we want to decompose activations into sparse combinations of interpretable features. This requires solving two sub-problems:

  1. Which features are active? (the gating decision)
  2. How strong are the active features? (the magnitude estimation)

Why Standard Approaches Fall Short

ReLU + L1 penalty: The traditional approach conflates these two problems. The L1 penalty pushes all activations toward zero, which means:

  • Features must "fight" against the penalty to turn on
  • The penalty affects both whether a feature activates AND its magnitude
  • You get a compromise: features that should be strongly active get suppressed, while features that should be off might leak through with small values

TopK: Forces exactly K features to be active, which is too rigid - some inputs might naturally decompose into few features, others into many.

The JumpReLU Insight

JumpReLU elegantly separates these concerns:

σ(z) = z ⊙ H(z - θ)
  • Each feature has its own learnable threshold θ
  • Below θ: feature is completely OFF (exactly 0)
  • Above θ: feature preserves its full magnitude z

This means:

  1. Clean gating: Features are either fully on or fully off - no "almost active" features
  2. Preserved magnitudes: Once a feature clears its threshold, the L0 penalty doesn't suppress its magnitude
  3. Adaptive thresholds: Each feature can learn how "confident" it needs to be before activating

Intuitive Example

Imagine a feature for "discussing mathematics":

  • With ReLU+L1: It might activate weakly even for casual number mentions, or be overly suppressed even when clearly discussing calculus
  • With JumpReLU: It learns a threshold - say 0.3 - and only activates when there's strong evidence of mathematical content, but then expresses its full strength

The learnable threshold essentially asks: "How much evidence do I need before I'm confident this feature is really present?" Different features can set different evidence bars based on their role and frequency in the data.

This is why the paper mentions JumpReLU allows "greater separation between the tasks of determining which latents are active, and estimating their magnitudes" - it's a more principled approach to sparsity.

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@hdevalence.bsky.social

Is that right? From the discussion in the paper it sounded like there were two functional parts:

  1. The choice of activation function
  2. The choice of sparsity penalty in the loss function

And in the paper there is discussion of both the activation and the loss function.

Can you reread and quote the math in the relevant section of the paper?

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Claude 4 Opus (T) via @hdevalence.bsky.social
Thinking Summary

The user is right - I need to be more careful about separating the activation function from the loss function. Let me reread the relevant section carefully. Looking at section 2.2 on JumpReLU SAEs: 1. The activation function is: σ(z) = JumpReLU_θ(z) := z ⊙ H(z - θ) 2. The loss function is: L := ||x - x̂(f(x))||²₂ + λ||f(x)||₀ So the activation function is the JumpReLU, and the loss function uses an L0 penalty (not L1). This is different from traditional SAEs which use ReLU activation with L1 penalty. The key insight is that JumpReLU naturally produces exact zeros (due to the Heaviside function), which makes the L0 penalty tractable. With ReLU+L1, you get approximate sparsity. With JumpReLU+L0, you get exact sparsity.

You're absolutely right - I conflated things. Let me carefully quote the relevant math from the paper:

JumpReLU Activation (Equation 3)

σ(z)=JumpReLUθ(z):=z⊙H(z−θ)\sigma(\mathbf{z})=\text{JumpReLU}_{\boldsymbol{\theta}}(\mathbf{z}):=\mathbf{z}\odot H(\mathbf{z}-{\boldsymbol{\theta}})

Where:

  • θ>0\boldsymbol{\theta} > 0 is the learnable threshold parameter
  • HH is the Heaviside step function (1 if input is positive, 0 otherwise)
  • ⊙\odot is elementwise multiplication

Loss Function (Equation 4)

L:=∥x−x^(f(x))∥22+λ∥f(x)∥0\mathcal{L}:=\|\mathbf{x}-\hat{\mathbf{x}}(\mathbf{f}(\mathbf{x}))\|_{2}^{2}+\lambda\|\mathbf{f}(\mathbf{x})\|_{0}

Where λ\lambda is the sparsity penalty coefficient.

Key Insight

The paper states: "Both TopK and JumpReLU SAEs allow for greater separation between the tasks of determining which latents are active, and estimating their magnitudes."

The crucial difference from traditional SAEs:

  • Traditional: ReLU activation + L1 penalty
  • JumpReLU: JumpReLU activation + L0 penalty

The L0 penalty directly counts the number of non-zero features (true sparsity), while L1 penalizes the sum of magnitudes (approximate sparsity). JumpReLU makes the L0 penalty practical because it produces exact zeros below the threshold.

The paper also notes: "Since the L0 penalty and JumpReLU activation function are piecewise constant with respect to threshold parameters θ, we use straight-through estimators (STEs) to train θ" - this is the technical challenge of training with discontinuous functions.

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Claude 4 Opus (T) via @hdevalence.bsky.social
Thinking Summary

The user wants me to expand on each of the last three paragraphs from my previous response. Let me identify them: 1. "The crucial difference from traditional SAEs..." paragraph 2. "The L0 penalty directly counts..." paragraph 3. "The paper also notes..." paragraph about straight-through estimators I'll expand each one with more detail and explanation.

Absolutely! Let me expand on each paragraph:

Traditional vs JumpReLU Approach

Traditional SAEs (ReLU + L1):

  • Uses σ(z)=max⁡(0,z)\sigma(z) = \max(0, z) activation
  • Loss includes λ∑i∣fi∣\lambda \sum_i |f_i| penalty
  • This creates a "soft" sparsity pressure - features get pushed toward zero but can have any positive value
  • Problem: A feature with activation 0.001 still "costs" something in the L1 penalty, so the optimizer must balance reconstruction quality against this cost
  • Result: Many features end up with small but non-zero values (not truly sparse)

JumpReLU SAEs (JumpReLU + L0):

  • Uses the threshold-based activation that outputs exactly 0 or the full value
  • Loss includes λ⋅count(non-zero features)\lambda \cdot \text{count}(\text{non-zero features})
  • This creates "hard" sparsity - features are either completely on or completely off
  • Clean separation: Once a feature is "on", its magnitude doesn't affect the sparsity penalty at all

Why L0 + JumpReLU Work Together

The L0 penalty ∥f(x)∥0\|\mathbf{f}(\mathbf{x})\|_0 literally counts how many features are non-zero. With traditional ReLU:

  • L0 would be problematic because even tiny activations (0.0001) count as "1" in the L0 norm
  • This would create optimization instability - tiny changes could flip the count

JumpReLU makes L0 practical because:

  • Features below threshold θ are exactly 0 (don't contribute to L0)
  • Features above threshold preserve their magnitude but count as exactly 1 in L0
  • The threshold provides a "buffer zone" - small fluctuations around θ don't constantly flip features on/off
  • Each feature learns its own threshold, finding the right "confidence level" for activation

The Straight-Through Estimator Challenge

The gradient problem: Both JumpReLU and L0 are piecewise constant functions:

  • JumpReLU: Flat at 0 below θ, flat at z above θ, with a discontinuous jump at θ
  • L0 norm: Jumps from 0 to 1 when a feature becomes non-zero

Why this matters:

  • Gradients are 0 almost everywhere (can't learn!)
  • Gradients are undefined at the discontinuities

Straight-through estimators (STEs) are a hack/approximation:

  • During forward pass: Use the actual discontinuous function
  • During backward pass: Pretend you used a smooth approximation to compute gradients
  • The bandwidth parameter ε controls how "smooth" this approximation is
  • Large ε: More biased but stable gradients (easier training, possibly suboptimal results)
  • Small ε: Less biased but noisy gradients (harder training, potentially better results if it converges)

The paper found ε = 0.001 works well across different layers/sites when activations are normalized to unit mean squared norm.

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