dpo

Talkie-1930-13b-it is a 13B parameter instruction-tuned language model trained on pre-1931 text and fine-tuned using reinforcement learning with DPO.

This paper investigates where and why output diversity collapses during post-training of language models, analyzing three OLMo 3 lineages (Think, Instruct, RL-Zero) across multiple tasks and metrics. The authors find that diversity collapse is primarily determined by training data composition and embedded in model weights during training, not addressable at inference time alone.

GroupDPO introduces a memory-efficient algorithm for group-wise direct preference optimization that leverages multiple candidate responses per prompt while reducing peak memory usage through decoupled backpropagation. The method demonstrates consistent improvements over standard DPO across offline and online alignment settings.