Gemma 4 E2b Nla L23 Av Grpo Dense500

--- license: gemma base_model: google/gemma-4-E2B tags: [interpretability, natural-language-autoencoder, activation-verbalizer, lora, grpo] --- # Gemma 4 E2B NLA Activation Verbalizer, GRPO-improved (dense-similarity reward, 500 steps, seed 0) LoRA adapter for an Activation Verbalizer (AV) over google/gemma-4-E2B layer-0/embedding injection (marker token U+3297, injection scale sqrt(1536)). Starting point was the prior-deviation SFT checkpoint (step 5000); this adapter adds 500 steps of GRPO against a dense semantic-similarity reward (MiniLM cosine between the generated description and the activation's source document). ## Results (held-out, n=580 single-row documents, greedy decode) - Exact-document top-1 retrieval improved from 3x chance (base) to 0.0155 (9x chance), worst-case p across 4 negative-sampling seeds 0.0003. - Improvement replicated in two independent training seeds with dose-response (0 -> 200 -> 500 steps), transferred to a lexical (tf-idf) metric the reward never saw, and was confirmed by a blind judge from a different model family (28 of 36 decided comparisons, p = 6e-4). - Out-of-distribution (295 never-seen documents, 4 unseen domains) a topic-level ranking signal survives (mean own-doc percentile 0.376 vs base 0.428 vs 0.5 chance, paired dense-vs-base p = 0.008). The lead lives in the semantic-embedding metric; the lexical metric is flat OOD and exact top-1 is at chance for both models. Topic-level ranking generalizes to unseen domains; word-for-word document identification does not. Result data and figures for every claim: https://github.com/SolshineCode/gpu-poor-nla Full methodology paper is in preparation. ## Usage Load with PEFT on top of google/gemma-4-E2B, inject the activation at the ㊗ marker position in the input embeddings, normalized to L2 norm 39.19. Trained and evaluated entirely on consumer hardware (4 GB laptop GPU + two used Tesla M40s).

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