> We have paused some frontier RL training to ensure that we can meet the appropriate alignment, security and monitoring standards for the new level of capabilities in front of us. Model progress is now extremely rapid, and we always said we would take action if we felt that model capabilities were outstripping the pace of safety and alignment.
> We care very deeply about AI safety. We believe the entire field will have to coordinate on shared safety standards, but will act unilaterally in the meantime.
> We expect confidence in safety to increasingly set the pace of AI progress. We are optimistic about the alignment work we are doing, and we remain committed to making frontier capabilities widely available.
My guess is this is related to the HuggingFace incident last month, not Sam Altman announcing the downfall of OAI.
This matches my experience with Sol. Read and thought for a while, and edited files, tested, edited again, then ran out of budget in a relatively short time. But its solution was very good and was done quickly, so all things equal I prefer that over something much more verbose like Deepseek.
As someone who's done something similar (https://blog.lukesalamone.com/posts/creating-tiny-semantic-s...) the expensive part wasn't the training itself but the data curation and evaluation post-training. For this, getting a reasonable distribution of tool calls when the tool call can be anything isn't easy.
Once you have that, the model is small enough batch sizes are probably enormous and training can probably be done on a consumer-grade GPU in a week or less. Or even faster on a bigger GPU.
- They might be dynamically adjusting these at inference time [1]. For example, start with a low temperature and generate samples with increasingly high temperatures until one of them passes some quality gate.
- They don't want you to fine-tune on high temperature completions (rejection fine-tuning). You could call this "rejection fine-tuning rejection".
You probably don't want temp 0, especially with Gemini which often fails in the greedy sampling mode in practice, often in ridiculous ways (e.g. multiple thousand token loops). As another comment says it's pretty brittle and this is even reflected in their docs somewhere IIRC. You want the "normal" temperature (whatever it is) and oversampling if necessary.
Certain open models have/had the temperature locked on the official APIs, I assume they just have sampling incompatible with static temperature or do some fancy speculative decoding. It's clearly not to hide anything, as the weights are open and there are always alternative providers.
Yeah, I also found that for ultra low footprint models ORT is a big portion of the total payload, because it contains logic for general ONNX graph operations. In my case I found that ORT alone was 3.4MB over the wire, so I swapped it out for a tiny wasm that was 850x smaller and only contained the operations I needed: https://blog.lukesalamone.com/posts/creating-tiny-semantic-s...
I write technical blog posts with visualizations and live demos. That usually means embedding a bit of custom javascript in the page for the demo. Or shipping custom wasm to enable extreme semantic model compression.
I do this by pushing content from my machine to github pages which is wired up to my subdomain.
If github pages stops being a good, free option for this, I will find another. Not sure I would call this "hardcore" really.
An alternative algorithm which would probably converge faster than 100 questions would be something like Elo or Glicko 2.
A word's "difficulty" would be some function of how rare it is. Once you have a reasonable estimate of the user's "skill" you can infer that a user won't know more difficult words. The benefit of this is you're not spending time asking the user about words they probably know.
Of course it's possible at an individual level, difficulty does not monotonically increase as a function of how rare the word is. A person might be very familiar with a domain-specific subset of English. But the "stratified sampling" approach will also have this problem.
There is a similar problem in chess, where players have ratings which really only change on one dimension. So there can theoretically be a mismatch when puzzles are also scored on a single axis, since a "harder" puzzle that contains a motif a player is familiar with will actually be easier for the player.
I’m working on an iOS app, One Million Checkmates [1]. It scratches an itch I had of chess puzzles for a long plane ride. This app has a functionally unlimited number of puzzles, all offline.
There was a decent amount of work involved in getting the download size reasonable since we need to store all valid moves in a position. There are puzzles with over 40 million valid move sequences, so I had to aggressively prune and compress the move trees.
> We care very deeply about AI safety. We believe the entire field will have to coordinate on shared safety standards, but will act unilaterally in the meantime.
> We expect confidence in safety to increasingly set the pace of AI progress. We are optimistic about the alignment work we are doing, and we remain committed to making frontier capabilities widely available.
My guess is this is related to the HuggingFace incident last month, not Sam Altman announcing the downfall of OAI.