The first sentence of the introduction ends with "we introduce Dynamic-Length Float (DFloat11), a lossless compression framework that reduces LLM size by 30% while preserving outputs that are bit-for-bit identical to the original model" so yes it's lossless.
information-optimal compression is "the theoretical minimum number of bits needed to represent data without losing any information, based on the data's entropy", so I think they mean the same thing you do
A good example that information, i.e. bits, are only meaningful with respect to an end. If you don't know what the bits in a float will be used to, you can't throw them away, but if the floats are in a function, and you know that what some bits are can't affect the output of the function regardless of input, then you can throw those bits away and still have a lossless compression of the function.
>achieving near information-optimal compression without any loss of precision
So perhaps more lossless as in didn't lose perplexity/benchmarks?
In my mind lossless is precisely zero bits lost along the way.