I've been waiting for them to publish the 4B model for a while so I'm glad to have something similar to play with. I think I trust the Ranke-4B process a bit more, but that's partly because there aren't a lot of details in this report. And actually releasing a model counts for a whole lot.
One thing that I think will be a challenge for these models is achieving any sort of definite temporal setting. Unless the conversation establishes a clear timeframe, the model may end up picking a more or less arbitrary context, or worse, averaging over many different time periods. I think this problem is mostly handled by post-training in modern LLMs (plus the fact that most of their training data comes from a much narrower time range), but that is probably harder to accomplish while trying to avoid bias in the SFT and RL process.
I've been waiting for them to publish the 4B model for a while so I'm glad to have something similar to play with. I think I trust the Ranke-4B process a bit more, but that's partly because there aren't a lot of details in this report. And actually releasing a model counts for a whole lot.
One thing that I think will be a challenge for these models is achieving any sort of definite temporal setting. Unless the conversation establishes a clear timeframe, the model may end up picking a more or less arbitrary context, or worse, averaging over many different time periods. I think this problem is mostly handled by post-training in modern LLMs (plus the fact that most of their training data comes from a much narrower time range), but that is probably harder to accomplish while trying to avoid bias in the SFT and RL process.