* Re-initialize ARA's lora_A deterministically
ARA optimizes both LoRA matrices in place, but the model reset between
trials only zeroes lora_B. Each trial therefore started its
optimization from whatever lora_A the previous trial left behind.
Restoring a trial after the study re-optimized it from a different
starting point, so the exported model was not the one that earned the
trial's scores, and reproduction could not match hashes.
Re-initialize lora_A for every optimized module from a generator
seeded with the run seed. This makes the starting point independent of
trial history, the device and the global RNG state.
* Zero lora_A when resetting the model
The fast reset path only zeroed lora_B, and a fresh adapter gets lora_A
from PEFT's random init. Modules a trial doesn't modify therefore kept
whatever lora_A an earlier trial left, or a random one. The model is
unaffected since B is zero, but saved adapters differed byte-for-byte
depending on trial history, breaking hash verification for adapter
exports.
Zero lora_A as well, both on reset and right after applying LoRA.
