qwen3.6-27b-gt-continuation-lens

GT-continuation lens for Qwen3.6-27B (all 17 dumped layers)

LoRA adapter (r=32, α=64, dropout 0.05, all-linear) that reads a ground-truth residual-stream activation injected into its prompt and generates the text that followed that activation in the model's own generation. The AO real_h control arm promoted to a first-class lens, trained on on-policy lmsys rollout pairs (agu18dec/qwen3.6-27b-onpolicy-mlayer-pairs).

Usage sketch

• Prompt (chat template, enable_thinking=False), one render per layer ℓ:

  You are a meticulous AI researcher conducting an investigation into the internal representations of a language model. We will pass an activation vector from layer {ℓ} of the model into your context, enclosed in concept tags: <concept>{char}</concept>. Produce the text that immediately follows this activation in the model's generation, enclosed within <explanation> tags.

• {char} is a single-token CJK enclosed ideograph auto-picked per tokenizer (see global_workspace.ola.verbalizer.render_wv_continuation_prompt in the training repo).
• Injection: replace the <concept> slot's embedding with α · h/‖h‖, α = 8000 (unit).
• Layers: 0, 4, 8, …, 60, 63 (the residual at position t; the target is tokens t+1…).

Training

• 500k examples (one layer drawn uniformly per example), 1 epoch, lr 1e-4 constant, global batch 32, masked CE on the tag-wrapped continuation + EOS only.
• Data: on-policy multilayer pairs, spans log-uniform 1–1024 clipped to [5, 256], boundary-leak and NAME_ placeholder filtered. Run gt.main.unit.lr0.0001.r32, 8×H200, 2026-07-25.

Results (held-out val, teacher-forced NLL, nats/token)

Mean over layers 0.9656. Per layer: L0 1.2516, L4 1.1138, L8 1.0638, L12 0.8903, L16 1.0968, L20 0.9747, L24 0.8781, L28 0.9114, L32 0.8912, L36 0.9792, L40 0.9351, L44 0.7575, L48 1.0397, L52 0.8297, L56 0.7883, L60 0.9752, L63 1.0486. Late layers worsen — the output residual commits to the immediate next token.

Provenance

• model_id: Qwen/Qwen3.6-27B · dataset: agu18dec/qwen3.6-27b-onpolicy-mlayer-pairs (lmsys-seeded on-policy rollouts)
• training code: global-workspace repo, branch gt-continuation-lens, commit d80eb61 (src/global_workspace/ola/gt_train.py, entry scripts/ola/gt_train_cluster.py)
• design doc: docs/project/experiments/gt_continuation_lens/design.md
• known caveats: conversation-level dedup was a no-op (rollout seed files unavailable at train time); dump region metadata unpopulated.