QwQ-32B: Embracing the Power of Reinforcement Learning

Re:QwQ-32B: Embracing the Power of Reinforcement Learning by flutetornado on Wednesday March 05, 2025 @08:10PM (#43274970)

My understanding is that top_k and top_p are two different methods of decoding tokens during inference. top_k=30 considers the top 30 tokens when selecting the next token to generate and top_p=0.95 considers the top 95 percentile. You should need to select only one.

https://github.com/ollama/ollama/blob/main/docs/modelfile.md...

Edit: Looks like both work together. "Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)"

Not quite sure how this is implemented - maybe one is preferred over the other when there are enough interesting tokens!

[ Parent | on HN ]

Re:QwQ-32B: Embracing the Power of Reinforcement Learning by nodja on Thursday March 06, 2025 @01:02AM (#43276884)

They both work on a sorted list of tokens by probability. top_k selects a fixed amount of tokens, top_p selects the top tokens until the sum of probabilities passes the threshold p. So for example if the top 2 tokens have a .5 and .4 probability, then a 0.9 top_p would stop selecting there.

Both can be chained together and some inference engines let you change the order of the token filtering, so you can do p before k, etc. (among all other sampling parameters, like repetition penalty, removing top token, DRY, etc.) each filtering step readjusts the probabilities so they always sum to 1.

[ Parent | on HN ]