Shiyuan Guo has not written any posts yet.
tl;dr: We fine-tune or few-shot LLMs to use reasoning encoded with simple ciphers (e.g. base64, rot13, putting a dot between each letter) to solve math problems. We find that these models only get an uplift from the reasoning (over directly answering) for very simple ciphers, and get no uplift for...
tl;dr: We fine-tune or few-shot LLMs to use reasoning encoded with simple ciphers (e.g. base64, rot13, putting a dot between each letter) to solve math problems. We find that these models only get an uplift from the reasoning (over directly answering) for very simple ciphers, and get no uplift for intermediate-difficulty ciphers that they can translate to English. This is some update against LLMs easily learning to reason using encodings that are very uncommon in pretraining, though these experiments don’t rule out the existence of more LLM-friendly encodings.
Research done as part of the Anthropic Fellows Program. Compute was generously funded by Scale and the Anthropic Fellows program.
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