I like that this shows how hard even conceptually simple ideas are to achieve in fine-tuning LLMs. Even given a pretty good starting dataset, a decent starting model, etc. this appears to have been a challenge.
One thing it did make me think about was that these models are suitable for things that don't have a natural definitive answer. That is, picking the perfect card given a set of picks is probably combinatorially impossible to solve. But picking a good card given a set is possible and LLMs can approach human level performance.
I think this leads to a set of problems that current LLMs may be fine-tuned to solve.
One thing it did make me think about was that these models are suitable for things that don't have a natural definitive answer. That is, picking the perfect card given a set of picks is probably combinatorially impossible to solve. But picking a good card given a set is possible and LLMs can approach human level performance.
I think this leads to a set of problems that current LLMs may be fine-tuned to solve.