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Forget costly supervised fine-tuning鈥攖his new contrastive reward mechanism enables efficient zoom-in tool learning in MLLMs, outperforming traditional methods.
Replacing just 12% of traditional training data with OctoLong's curated code contexts leads to substantial improvements in long-range retrieval and state tracking for language models.
