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Continual Learning Futures

Chart pathways beyond static language models by integrating continual learning, hybrid architectures, and on-the-job adaptation.

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Continual learning workflows

  • Data intake: Stream in observations from production or curated feeds. Filter for quality, compliance, and novelty.
  • Candidate updates: Generate proposed fine-tunes or adapter training runs using small batches.
  • Safety gating: Run pre-update evaluations (toxicity, bias, hallucination tests) to block risky updates.
  • Deployment: Apply updates gradually—shadow traffic, region-limited release, or user opt-in.
  • Monitoring: Track performance on historical benchmarks, new data, and safety metrics.
  • Rollback: Maintain snapshot versions for rapid reversion if issues arise.
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