Thank you for sharing! Sharing best practices like this is quite nice!
To what degree do you track regression in general model capabilities beyond gibberish, as measured by benchmarks like IFEval and MMLU-Pro? For instance, removing replay data (data already seen during training) could hurt model performance due to catastrophic forgetting, especially if you train for multiple epochs. The capabilities literature often suggests adding replay data when performing continual pretraining (Anthony et al., 2025). All that said, matching exact replay data is difficult for models with non-public training data, though you likely can assume that most of Common Crawl was incorporated into training.
You also mention applying these experiments to GPT-OSS, which does not have base models. Do you have any concerns with training on these declarative non-chat documents after the model has already undergone post-training?
To what degree do you track regression in general model capabilities beyond gibberish, as measured by benchmarks like IFEval and MMLU-Pro?
We have not been tracking model capabilities as part of our research, as we have been mostly interested in changes in behavior as part of alignment testing. But I got Claude to run IFEval and MMLU-Pro on some of the SDF models we have trained, which you can see below. So performance regression is clearly something to keep in mind.
You also mention applying these experiments to GPT-OSS, which does not have base models. Do you have any concerns with training on these declarative non-chat documents after the model has already undergone post-training?
It is definitely not ideal to do continued pre-training at the end of post-training! You clearly see artifacts from this like increased rates of gibberish, and the performance regression above. But it is quite useful for getting the models to act on false information.
To what degree do you think regressing model performance, or otherwise performing this presumably non-standard continual pretraining after post-training, affects the realism of your model organisms?
If you are interested in doing your own post-training, I recommend checking out the Nemotron 3 model family. Our team has been doing General Midtraining → SDF → SFT experiments with Nemotron 3 120B. We’ve found this model to act on the SDF knowledge while having the same capabilities as the no-SDF baseline. We have a forthcoming paper that uses this methodology.
Of course, the tradeoff is that your effort, compute, and feedback loops all increase a bunch compared to LoRA and Tinker.
Thanks for the pointer, thats quite useful. Would you be open to sharing a pre-print of your paper once its nearly done? I’d be super curious to see how exactly you do Mitraining, SDF, SFT. If yes feel free to reach out to jeremy@apolloresearch.ai.
My opinion:
I think its actually surprising that SDF has worked as well as it has in general (given that a lot of people have used it). Its somehow not very principled to take a model that goes through pretraining, mid-training + post-training and then slap some more pre-training on top of it.
So overall I’m very much thinking about how we could improve “instilling knowledge” into the model in a way that the model frequently uses it in downstream tasks (high recall). In a way this is basically a mid-training problem, i.e. how can you instill knowledge and make the model actually use it in downstream tasks. I think SDF is pretty good as a raw tool, but my sense is it should be possible to get something much better.
Thank you for sharing! Sharing best practices like this is quite nice!
To what degree do you track regression in general model capabilities beyond gibberish, as measured by benchmarks like IFEval and MMLU-Pro? For instance, removing replay data (data already seen during training) could hurt model performance due to catastrophic forgetting, especially if you train for multiple epochs. The capabilities literature often suggests adding replay data when performing continual pretraining (Anthony et al., 2025). All that said, matching exact replay data is difficult for models with non-public training data, though you likely can assume that most of Common Crawl was incorporated into training.
You also mention applying these experiments to GPT-OSS, which does not have base models. Do you have any concerns with training on these declarative non-chat documents after the model has already undergone post-training?
We have not been tracking model capabilities as part of our research, as we have been mostly interested in changes in behavior as part of alignment testing. But I got Claude to run IFEval and MMLU-Pro on some of the SDF models we have trained, which you can see below. So performance regression is clearly something to keep in mind.
It is definitely not ideal to do continued pre-training at the end of post-training! You clearly see artifacts from this like increased rates of gibberish, and the performance regression above. But it is quite useful for getting the models to act on false information.
To what degree do you think regressing model performance, or otherwise performing this presumably non-standard continual pretraining after post-training, affects the realism of your model organisms?
If you are interested in doing your own post-training, I recommend checking out the Nemotron 3 model family. Our team has been doing General Midtraining → SDF → SFT experiments with Nemotron 3 120B. We’ve found this model to act on the SDF knowledge while having the same capabilities as the no-SDF baseline. We have a forthcoming paper that uses this methodology.
Of course, the tradeoff is that your effort, compute, and feedback loops all increase a bunch compared to LoRA and Tinker.
Thanks for the pointer, thats quite useful. Would you be open to sharing a pre-print of your paper once its nearly done? I’d be super curious to see how exactly you do Mitraining, SDF, SFT. If yes feel free to reach out to jeremy@apolloresearch.ai.
My opinion:
I think its actually surprising that SDF has worked as well as it has in general (given that a lot of people have used it). Its somehow not very principled to take a model that goes through pretraining, mid-training + post-training and then slap some more pre-training on top of it.
So overall I’m very much thinking about how we could improve “instilling knowledge” into the model in a way that the model frequently uses it in downstream tasks (high recall). In a way this is basically a mid-training problem, i.e. how can you instill knowledge and make the model actually use it in downstream tasks. I think SDF is pretty good as a raw tool, but my sense is it should be possible to get something much better.