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Transformer-based language models have shown promise in genomics but face challenges unique to DNA, such as sequence lengths spanning hundreds of millions of base pairs and subtle long-range dependencies. Although next-token prediction remains the predominant pre-training objective (inherited from NLP), recent research suggests that multi-objective frameworks can better capture complex structure. In this work, we explore whether the Birdie framework, a reinforcement learning-based, mixture-of-objectives pre-training strategy, can similarly benefit genomic foundation models. We compare a slightly modified Birdie approach with a purely autoregressive, next token prediction baseline on standard Nucleotide Transformer benchmarks. Our results show performance gains in the DNA domain, indicating that mixture-of-objectives training could be a promising alternative to next token prediction only pre-training for genomic sequence modeling.