ProofOfConcept d6b85d204a research: on-policy beats off-policy, DPO failure modes, variant landscape ... On-policy rejected examples (model's own failures) are better training signal than off-policy (pre-collected). Our temperature sweep is on-policy by construction. DPO can accidentally reduce preferred likelihood (DPOP fixes this). Multiple DPO variants exist — start with ORPO, switch only if specific failure modes observed.		2026-03-31 03:19:27 -04:00
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checkpoint	checkpoint: sync live weights back into model safetensors in-place	2026-03-30 22:55:23 -04:00
research	research: on-policy beats off-policy, DPO failure modes, variant landscape	2026-03-31 03:19:27 -04:00
apollo_mini.py	apollo: rewrite optimizer from paper's math + add research analysis	2026-03-31 00:54:17 -04:00
apollo_worker.py	apollo: make rank configurable (default 1 = Mini, higher ranks for experimentation)	2026-03-30 22:06:31 -04:00
DESIGN.md	DESIGN.md: complete rewrite reflecting validated architecture	2026-03-31 00:42:53 -04:00
export_weights.py	apollo-mini training system: initial implementation	2026-03-30 22:02:37 -04:00
extract_steering_vector.py	steering vector extraction script — answering Q5 experimentally	2026-03-31 02:28:18 -04:00
first_training_step.py	first_training_step.py: ready for Kent to run	2026-03-31 01:59:52 -04:00
start_vllm_with_apollo.sh	vllm launcher with apollo hook	2026-03-30 22:24:02 -04:00
train.py	apollo-mini training system: initial implementation	2026-03-30 22:02:37 -04:00
training_example.py	apollo-mini training system: initial implementation	2026-03-30 22:02:37 -04:00
vllm_export_hook.py	apollo-checkpoint: efficient diff-based GPU weight checkpointing	2026-03-30 22:53:17 -04:00
weight_mapping.py	weight_mapping: strip language_model prefix to match HF text model names	2026-03-30 23:11:03 -04:00