Learning to Reason with LLMs
openai.com
Learning to Reason with LLMs
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Re: Learning to Reason with LLMs
#2Re: Learning to Reason with LLMs
#3Damn, that looks like a big jump.
Re: Learning to Reason with LLMs
#4Generating more "think out loud" tokens and hiding them from the user...
Idk if I'm "feeling the AGI" if I'm being honest.
Also... telling that they choose to benchmark against CodeForces rather than SWE-bench.
Re: Learning to Reason with LLMs
#5What's with this how many r's in a strawberry thing I keep seeing?
Re: Learning to Reason with LLMs
#6[deleted]
Re: Learning to Reason with LLMs
#7What's with this how many r's in a strawberry thing I keep seeing?
It’s a common LLM riddle. Apparently many fail to give the right answer.
Re: Learning to Reason with LLMs
#8The model performance is driven by chain of thought, but they will not be providing chain of thought responses to the user for various reasons including competitive advantage.
After the release of GPT4 it became very common to fine-tune non-OpenAI models on GPT4 output. I’d say OpenAI is rightly concerned that fine-tuning on chain of thought responses from this model would allow for quicker reproduction of their results. This forces everyone else to reproduce it the hard way. It’s sad news for open weight models but an understandable decision.
Re: Learning to Reason with LLMs
#9What's with this how many r's in a strawberry thing I keep seeing?
LLM are bad at answering that question because inputs are tokenized.
Re: Learning to Reason with LLMs
#10What's with this how many r's in a strawberry thing I keep seeing?
Models don't really predict the next word, they predict the next token. Strawberry is made up of multiple tokens, and the model doesn't truely understand the characters in it... so it tends to struggle.