perplexity
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c3b455bd9c
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feat: Phase 3.5 — DPO training pair generation from Deep Dive pipeline
Wire arXiv relevance filter output directly into DPO pair generation,
closing the loop between research synthesis and overnight training data.
New module: dpo_generator.py
- DPOPairGenerator class with 3 pair strategies:
* summarize: paper → fleet-grounded analysis (chosen) vs generic (rejected)
* relevance: 'what matters to Hermes?' → scored context vs vague
* implication: 'what should we do?' → actionable insight vs platitude
- Extracts synthesis excerpts matched to each ranked item
- Outputs to ~/.timmy/training-data/dpo-pairs/deepdive_{timestamp}.jsonl
- Format: {prompt, chosen, rejected, task_type, evidence_ids,
source_session, safety_flags, metadata}
Pipeline changes (pipeline.py):
- Import DPOPairGenerator with graceful degradation
- Initialize from config deepdive.training.dpo section
- Execute as Phase 3.5 between synthesis and audio
- DPO results included in pipeline return dict
- Wrapped in try/except — DPO failure never blocks delivery
Config changes (config.yaml):
- New deepdive.training.dpo section with:
enabled, output_dir, min_score, max_pairs_per_run, pair_types
Integration tested: 2 mock items × 3 pair types = 6 valid JSONL pairs.
Chosen responses consistently richer than rejected (assert-verified).
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2026-04-15 21:24:01 -04:00 |
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Ezra (Archivist)
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4b1873d76e
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feat(deepdive): production briefing prompt + prompt engineering KT
Deploy Nexus / deploy (push) Has been cancelled
- production_briefing_v1.txt: podcast-script prompt engineered for
10-15 min premium audio, grounded fleet context, and actionable tone.
- PROMPT_ENGINEERING_KT.md: A/B testing protocol, failure modes,
and maintenance checklist.
- pipeline.py: load external prompt_file from config.yaml.
Refs #830
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2026-04-05 20:19:20 +00:00 |
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