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## Summary The user raised a critical question about data scaling for the 1.2B-parameter EAGLE-3 draft model, noting that with only ~21M tokens of unique training data (10K samples), the model might be severely data-limited. The assistant analyzed the current training run (epoch 4 of 5, ~74% complete) and observed diminishing returns in validation metrics (loss plateauing at ~6.13, step-0 accuracy at ~74.5%). Drawing on the EAGLE-3 paper's scaling laws (which show gains up to 8× more data) and the concept of grokking (overtraining on small datasets to force generalization), the assistant discussed two paths: generating 5-10× more data or running a 100-epoch grokking continuation with constant learning rate. The user opted to benchmark the current checkpoint first. When launching SGLang with the newly trained EAGLE-3 checkpoint, the server exhibited the same broken behavior as the previous round: **accept_len ~1.00, accept_rate ~0.20** (zero draft tokens accepted). Debugging revealed two critical issues: first, a **weight key name mismatch** — the speculators library saves the decoder layer as `layers.0.*` but SGLang's `LlamaForCausalLMEagle3` expects `midlayer.*`, causing the trained weights to be silently dropped during loading. This was fixed by renaming keys. Second, and more fundamentally, **the hidden states passed to the draft model are 7168-dimensional instead of the expected 21504-dimensional concatenation of three auxiliary layer hidden states**. The `fc` fusion layer (which projects 21504 → 7168) is never applied because the shape check `hidden_states.shape[-1] != embeds.shape[-1]` evaluates to `7168 != 7168` → False, bypassing the fusion entirely. The root cause is that `eagle_use_aux_hidden_state` is not being properly activated for the KimiK25 model, or the target model's `capture_aux_hidden_states` mechanism is not producing the multi-layer hidden states that the draft model was trained on. This explains why both the old vLLM-trained drafter and the new SGLang-trained drafter exhibit identical zero-acceptance behavior — they both receive single-layer hidden states at inference time despite being trained on fused multi-layer features.

The Data Scaling Paradox: When 21 Million Tokens Wasn't the Problem 2574 words

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