[HN Gopher] Sky-T1: Train your own O1 preview model within $450
       ___________________________________________________________________
        
       Sky-T1: Train your own O1 preview model within $450
        
       Author : fofoz
       Score  : 22 points
       Date   : 2025-01-13 08:51 UTC (14 hours ago)
        
 (HTM) web link (novasky-ai.github.io)
 (TXT) w3m dump (novasky-ai.github.io)
        
       | elashri wrote:
       | > We initially trained a 32B model using 3-4K math problems from
       | the Numina dataset (provided by STILL-2), achieving a significant
       | improvement in AIME24 accuracy from 16.7% to 43.3%. However, when
       | we incorporated coding data generated from the APPs dataset into
       | the training process, AIME24 accuracy dropped to 36.7%. We
       | hypothesize that this decline is due to the distinct reasoning
       | approaches required for math and coding tasks.
       | 
       | This is interesting. For large models that were trained on much
       | more data. I wonder if o1 is trained in a different way that
       | GPT-4o. Do they only rely on synthetic data (plus some hand
       | crafted datasets). But then how would O1 knows a lot of facts
       | like GPT-4o indicating that these were in the training.
       | 
       | Can someone with more understanding and knowledge weight on this?
        
       | zamadatix wrote:
       | Fine tune an existing model. Training such a model so cheaply
       | would've been nuts.
        
       | ipsum2 wrote:
       | For math only.
        
       | thot_experiment wrote:
       | They finetuned QwQ to perform well on a benchmark. For the past
       | two years there has been a constant stream of "X fine-tune beats
       | Y closed model on Z benchmark". This isn't interesting and has
       | never been interesting, see Goodhart's Law. If you're actually
       | using local models day to day you will quickly find that
       | finetunes are almost universally a waste of time. Even when it
       | comes to something like smutty roleplay gaslighting a model can
       | often lead to more interesting and consistent results because
       | finetunes are basically always overfit to the training data.
        
       ___________________________________________________________________
       (page generated 2025-01-13 23:03 UTC)