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Marius CiepluchandClaude Opus 5
0db9b378d9
SIMBA ranks instances by max-to-min score gap across variants, which turns our noise floor from an obstacle into an instance sampler: the 11 injection pairs that disagree with themselves are the pairs where a rule change can flip an outcome. Its two alternating strategies -- append a demo, append a rule -- are the split our levers already have. GEPA's reflection meta-prompt is a specification for the feedback function, not just a prompt: it asks the reflector to extract domain facts the model cannot re-derive later, which means feedback must carry which sink, which span and which side leaked, not a verdict. And the finding that outranks both: GEPA splits a trainset, SIMBA batches 32. Injection has 12 train teachers. These algorithms are sample-efficient in rollouts, not in instances, and they will not shortcut a corpus of twelve. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG