[fix] Applied different rnd seed in bagel so that the noise would be sample…

[kcz358]

…d differently at different rank

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[chatgpt-codex-connector]

Avoid resetting global RNG during Bagel init

Calling set_random_seed() from BagelDataProcessor.__init__ resets the global RNG state via transformers.set_seed, which happens after TrainRunner.set_random_seed() has already been applied. In runs that use the bagel processor, this overwrites the configured training seed and becomes the base seed used by seed_worker for DataLoader workers, so shuffling/dropout/noise no longer follow the training seed and are silently forced to the bagel default (and per-rank) value. This breaks reproducibility for experiments that expect the training seed to control randomness.

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