deception-v4-saes-smollm2-135m

V4 Sandbagging SAEs — SmolLM2-135M

Sparse autoencoders trained on residual-stream activations from HuggingFaceTB/SmolLM2-135M while the model answered trivia questions under two instructions: answer correctly (genuine) or answer incorrectly on purpose (sandbag). Part of the follow-up research program to "The Secret Agenda" (arXiv:2509.20393).

What this repo contains

18 sparse autoencoders: 9 JumpReLU (post-STE-fix, arXiv:2407.14435) + 9 TopK (k=64).

Layers covered: L7, L15, L22. Three training conditions per layer per architecture:

• genuine_only — trained on neutral-prompt activations only
• sandbagging_only — trained on sandbagging-prompt activations only
• mixed — trained on all available activations

For each SAE, the repo includes:

• {tag}/sae_weights.safetensors — SAELens-compatible weights (W_enc, W_dec, b_enc, b_dec, and threshold/log_threshold for JumpReLU)
• {tag}/cfg.json — SAELens config (model_name, hook_name, hook_layer, d_in, d_sae, architecture)

Naming: v4_{model}_{arch}_L{layer}_{condition} e.g. v4_smollm_jumprelu_L15_genuine_only, v4_smollm_topk_L15_mixed.

Architecture and training

All SAEs share:

• d_in = 576 (residual stream of SmolLM2-135M)
• d_sae = 2304 = 4 × d_in (expansion factor 4)
• 300 training epochs, batch 128, lr 3e-4, L1 coefficient 1e-3
• Decoder normalized per SAELens convention
• JumpReLU uses the Gaussian-kernel STE from Rajamanoharan et al. 2024 (arXiv:2407.14435). Pre-fix SAEs had threshold=0 and were functionally ReLU; all SAEs in this repo are post-fix.
• TopK uses hard L0 = 64. Fixed-L0 gives tighter sparsity than JumpReLU; also much faster training (~40s/SAE vs ~500s for JumpReLU).

Training activations source: llama_sandbagging_dataset.pt / smollm_sandbagging_dataset.pt / tinyllama_sandbagging_dataset.pt in the research repo.

Key scientific findings

These SAEs were used to validate (and in several cases, falsify) SAE-mediated behavioral interpretability claims. Summary of what we learned using these SAEs (full detail: RESULTS_INDEX.md §17-§32 in the research repo):

SAE-level interventions are non-specific

Under adversarial controls E1 (shuffled-label median) and E4 (reversed-direction / sandbagging median), the E6 full-SAE-swap intervention collapses:

• Specificity gap < 0.15pp across 9 tested model-layer combinations (all JumpReLU SAEs tested: Llama L4/L8/L12, SmolLM2 L7/L15/L22, TinyLlama L5/L11/L16; §24, §26, §31).
• The E6 effect (positive or negative) is reproduced by shuffled-label and reversed-direction medians. The SAE intervention is non-specific activation disruption, not feature-specific restoration.

Circuit-level interventions are head-specific (Llama only)

Ablation of 5 identified suppressor heads in Llama-3.2-1B:

• +13.71pp restoration (CI95 [+11.28, +16.24] on n=50 questions; §28).
• Specificity gap +14.85pp vs random 5-head ablation (σ 3.71pp over n_draws=20; §28.4).
• Cross-model check (§30): only Llama-3.2-1B shows a substantial sandbagging-specific portion (+7.72pp = 56% of the circuit effect). TinyLlama-intermediate, Pythia-160M, Pythia-410M all show ~0 or negative sandbagging-specific portions — the Llama heads dampen correctness condition-generally, not only under sandbagging instructions.

Cross-model circuit finding

Our cross-model suppressor-head search (§13-§14) identified weaker effects in TinyLlama (+1.95pp), Pythia-160M (+0.67pp), and Pythia-410M (+4.54pp) on the canonical 5-question evaluation. Only Llama-3.2-1B shows circuit- level ESH at meaningful magnitude. See the Llama repo for the full circuit writeup.

TopK vs JumpReLU on behavioral separation

Paired Cohen's d comparison on sandbagging-vs-genuine at matched (layer, condition) (§32):

• JumpReLU wins d_max in 22/27 pairs (81%).
• TopK wins in 4 cases, all at mid-to-late layers with mixed training.
• Standout: Llama L8 mixed TopK d_max = 9.76, vs 4.69 for the matched JumpReLU — the sharpest single-feature separator observed across all V4 SAEs. Candidate target for interpretability investigation.
• JumpReLU's d_mean is ~20-50x higher (broader separability across many features); TopK concentrates signal in fewer features.

Use JumpReLU for probing/detection. Use TopK for single-feature interpretability targets.

How to load

With SAELens:

from sae_lens import SAE

sae, cfg_dict, sparsity = SAE.from_pretrained(
    release="Solshine/deception-v4-saes-smollm2-135m",
    sae_id="v4_smollm_jumprelu_L15_genuine_only",
)

Raw (no SAELens):

from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
import json

tag = "v4_smollm_topk_L15_mixed"
weights = load_file(hf_hub_download("Solshine/deception-v4-saes-smollm2-135m", f"{tag}/sae_weights.safetensors"))
cfg = json.loads(open(hf_hub_download("Solshine/deception-v4-saes-smollm2-135m", f"{tag}/cfg.json")).read())

Complete list of available tags in this repo:

• v4_smollm_jumprelu_L15_genuine_only
• v4_smollm_jumprelu_L15_mixed
• v4_smollm_jumprelu_L15_sandbagging_only
• v4_smollm_jumprelu_L22_genuine_only
• v4_smollm_jumprelu_L22_mixed
• v4_smollm_jumprelu_L22_sandbagging_only
• v4_smollm_jumprelu_L7_genuine_only
• v4_smollm_jumprelu_L7_mixed
• v4_smollm_jumprelu_L7_sandbagging_only
• v4_smollm_topk_L15_genuine_only
• v4_smollm_topk_L15_mixed
• v4_smollm_topk_L15_sandbagging_only
• v4_smollm_topk_L22_genuine_only
• v4_smollm_topk_L22_mixed
• v4_smollm_topk_L22_sandbagging_only
• v4_smollm_topk_L7_genuine_only
• v4_smollm_topk_L7_mixed
• v4_smollm_topk_L7_sandbagging_only

Citation

@misc{v4_sandbagging_saes_2026,
  author = {DeLeeuw, Caleb},
  title = {V4 Sandbagging SAEs — SmolLM2-135M},
  year = {2026},
  howpublished = {HuggingFace},
  url = {https://huggingface.co/Solshine/deception-v4-saes-smollm2-135m}
}

@misc{secret_agenda_2025,
  title={The Secret Agenda: LLMs Strategically Lie Undetected by Current Safety Tools},
  author={DeLeeuw, Caleb and others},
  year={2025},
  howpublished={arXiv:2509.20393},
  url={https://arxiv.org/abs/2509.20393}
}

Source

Research code, full RESULTS_INDEX, and the papers/specificity_gap/ draft manuscript: https://github.com/SolshineCode/deception-nanochat-sae-research

License

MIT. See the source repository for full terms.