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ProofOfConcept 3be20062d1 research: learning rate as trust calibration — how much to trust each example 
lr isn't speed, it's trust-per-example. At 27B, lr=1e-5 = ~270K
values adjusted per example. The coherent direction emerges from
many votes (examples). Apollo moments smooth the noise. DPO needs
lower lr because comparative votes are noisier than absolute votes.
2026-03-31 02:46:19 -04:00
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v0 research: distill and sift — SUMMARY of 7 real insights + 7 testable questions 2026-03-31 02:26:57 -04:00
apollo-paper-analysis.md apollo: rewrite optimizer from paper's math + add research analysis 2026-03-31 00:54:17 -04:00
context-frozen-training.md research: context-frozen training — gradient masking, memory analysis, GDN considerations 2026-03-31 00:59:04 -04:00
gdn-gradient-flow.md research: GDN gradient flow — disposition architecture in linear attention 2026-03-31 01:58:50 -04:00
gradient-flow-frozen-context.md research: gradient flow through frozen context + directional sharpness analysis 2026-03-31 01:03:22 -04:00
hogwild-convergence.md research: HOGWILD convergence theory — why lock-free concurrent training works 2026-03-31 00:58:02 -04:00
OPEN-QUESTIONS.md research: distill and sift — SUMMARY of 7 real insights + 7 testable questions 2026-03-31 02:26:57 -04:00
practical-intuitions.md research: learning rate as trust calibration — how much to trust each example 2026-03-31 02:46:19 -04:00
steering-vectors-bridge.md research: steering vectors — prototype behavioral changes before training 2026-03-31 02:19:50 -04:00
SUMMARY.md research: distill and sift — SUMMARY of 7 real insights + 7 testable questions 2026-03-31 02:26:57 -04:00
task-vectors-model-merging.md research: task vectors + model merging — version control for personality 2026-03-31 02:18:15 -04:00