Chunk 28.0

## Summary of This Chunk **Inference pipeline debugging and optimization** dominated this segment. First, the reasoning capture bug was identified: SGLang's `--reasoning-parser` wasn't configured, so `thinking` content was embedded in `message.content` with `reasoning_content: null`. The fix involved rewriting `run_inference.py` to bypass OpenAI's chat completions API entirely and use SGLang's `/generate` endpoint with raw `input_ids`/`output_ids` — pre-tokenizing prompts via `apply_chat_template` (which appends the `thinking` token 163606), then receiving the model's exact token sequence including the `response` token (163607) and native tool-call special tokens. This eliminated all parsing ambiguity and produced faithful training data. **Server throughput optimization** was the second major push. The KV cache was identified as the bottleneck (only ~50 concurrent requests fitting at 4K avg token length). Three levers were tried: `--mem-fraction-static 0.93` (OOM'd), `--kv-cache-dtype fp8_e4m3` (rejected by user as quality-degrading), and `--enable-hierarchical-cache` with `--hicache-size 48` (48GB/rank host RAM overflow). The winning config settled at `mem_fraction_static=0.88 + bf16 KV + hicache=48GB`, yielding 159K GPU tokens and ~930-1350 tok/s throughput — roughly 2-3x improvement over the initial 600 tok/s baseline. The monitor and stats collector were also fixed to properly display token counts from the new `/generate` response format. **Dataset size capping** was the final task. To avoid spending 57+ hours generating all 88K samples, `--max-tokens-per-dataset 10000000` was added to `run_inference.py`, targeting ~10M tokens per category (~92M total, ~17-26h estimated). The inference is now running steadily on B2_opencodeinstruct with the capped budget.

The Three Crises of an EAGLE-3 Training Pipeline: Reasoning Capture, Throughput Optimization, and Data Quality Assurance 2283 words

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