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Continual learning can create systems that compound with use
Continual learning can create systems that compound with use
Karanam describes continual learning as a path toward faster, better, and cheaper models, with the larger goal of creating systems whose capabilities compound as they are used.
- Speaker
- Arjun Karanam
- Topic
- Continual Learning
- Source timestamp
- 1:32
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- AI progress needs experience alongside model intelligence
- Agent interactions should become signals for future improvement
- Feedback should update either models or their surrounding harness
- Full agent traces should include tools and sub-agents
- Corrective behavior provides richer feedback than simple ratings
- Evals should closely reflect real production usage
- Agent tasks should be replayable for evaluation
- Harnesses should enable orchestration instead of rigid workflows
- Agents should access the same capabilities as users
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- Feedback scope determines where learned information should live
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