Blog: https://medium.com/@peakji/a-small-step-towards-reproducing-...
Hugging Face: https://huggingface.co/collections/peakji/steiner-preview-67...
Im wondering if we can abstract chain of thought further down into the computation levels to replace a lot of matrix multiply. Like smaller transformers with less parameters and more selection of which transformer to use through search.
More importantly, I highly recommend to try these out firsthand (not only Steiner, but all reasoning models). You'll find that these reasoning models can solve many problems that other models with the same parameter size cannot handle. The existing benchmarks may not reflect this well, as I mentioned in the article:
"... automated evaluation benchmarks, which are primarily composed of multiple-choice questions and may not fully reflect the capabilities of reasoning models. During the training phase, reasoning models are encouraged to engage in open-ended exploration of problems, whereas multiple-choice questions operate under the premise that "the correct answer must be among the options." This makes it evident that verifying options one by one is a more efficient approach. In fact, existing large language models have, consciously or unconsciously, mastered this technique, regardless of whether special prompts are used. Ultimately, it is this misalignment between automated evaluation and genuine reasoning requirements that makes me believe it is essential to open-source the model for real human evaluation and feedback."