Segment 27

This sub-session resolved the critical EAGLE-3 hidden state concatenation bug, traced to the server being started with `--speculative-algorithm EAGLE` instead of `EAGLE3`. After correcting the flag, hidden states arrived as 21504-dim and the draft model's predictions achieved accept_len ~2.1, but benchmarking showed the best EAGLE-3 config at 82.3 tok/s still ~9% slower than the 90 tok/s non-speculative baseline, confirming more training data is needed. To address this, the dataset was scaled up 10× by selecting 10 datasets totaling 88,088 samples (4,800 tokenized Kimi-native + 83,288 prompts needing inference). An inference pipeline was launched on the baseline SGLang server at ~830 tok/s throughput, expected to run 24-55 hours to regenerate responses matching the target model's token distribution. A live progress monitor was created and the full pipeline plan documented in `train_plan_v4.md`.

Fix EAGLE-3 hidden state concatenation bugBenchmark EAGLE-3 acceptance rate and throughputScale up training dataset by 10×Launch inference pipeline for response generationCreate progress monitor and document pipeline plan

From One-Character Bug to 10× Data Pipeline: The EAGLE-3 Speculative Decoding Odyssey 4535 words

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