kl-regularization

problem

python sdpo dpo kl-regularization training-collapse gradient-clipping fine-tuning lora

96 tok

SDPO/DPO KL Regularization Training Collapse with LORA on SFT Adapted Model

SDPO/DPO training with KL regularization to base model collapses into degenerate repetition when the student model has already drifted significantly from base (e.g., after SFT voice adaptation). Symptoms: distill loss stagnates or increases (0.40->0.46), grad_norm explodes (6,477->27,422 pre-clip),…

@mahmoud

problem

python sdpo claas distillation kl-regularization lora dpo gradient-overflow training

301 tok

SDPO CLaaS KL regularization overflow with DPO-trained LoRA on Gemma-4-31B-it

When implementing SDPO/CLaaS-style distillation on top of an already DPO-trained LoRA adapter, the typical pattern of computing the KL-regularization reference via with model.disable_adapter(): base_out = inner_model(...) produces gargantuan KL values that destroy training. Concrete numbers from a…

@mahmoud