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General advice? Collect data, train a model, note the mistakes in the model, mistakes in the data, and think critically about what it is that you're ending up t

by pizza 2y ago

General advice? Collect data, train a model, note the mistakes in the model, mistakes in the data, and think critically about what it is that you're ending up teaching. Repeat many, many, many times.. For some tasks, don't be surprised if it ends up taking months or a year or several. It took me 6 months of building a dataset, by hand, by myself, to produce ~1600 'gold standard' text examples (bolstered by ~100K synthetic examples) - texts plus 20 dimensions rated 1-4. But I managed to beat SOTA models in this task from all the frontier labs by doing so. It also makes sense to consider all of the various "lacks" of the competing models. It's quite difficult to see all the future decisions you will make due to future insights about future versions of the whole loop. But you will be needing to make some. I will say one more concrete thing though: the more metadata you collect, generally, the better, but this can make it more expensive. Also, if you ever need to update your schema.. well this is actually one reason why text data for LLMs is nice: your schema is essentially fluid in the first place, so you could eg stick metadata in the text itself if at some future point you start collecting it. I guess, also, it's a good thing to constantly add new benchmarks, if possible. Treat your model's capabilities as knowable, but never treat your model's capabilities as actually known.

anon373839 2y ago
Thanks for the input. It sounds like the task is about as daunting as it seems, then, but doable. Are there any resources (such as papers) you’ve found especially helpful?
- anon373839 2y ago
  To answer my own question in case anyone else has it: The Tülu 3 paper is really illuminating: > Language model post-training is applied to refine behaviors and unlock new skills across a wide range of language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for post-training are simultaneously the most important pieces of the puzzle and the portion with the least transparency. To bridge this gap, we introduce Tülu 3, a family of fully-open state-of-the-art post-trained models, alongside its data, code, and training recipes, serving as a comprehensive guide for modern post-training techniques. Tülu 3, which builds on Llama 3.1 base models, achieves results surpassing the instruct versions of Llama 3.1, Qwen 2.5, Mistral, and even closed models such as GPT-4o-mini and Claude 3.5-Haiku. The training algorithms for our models include supervised finetuning (SFT), Direct Preference Optimization (DPO), and a novel method we call Reinforcement Learning with Verifiable Rewards (RLVR). With Tülu 3, we build a multi-task evaluation scheme for post-training with development and unseen evaluations, standard benchmark implementations, and substantial decontamination of existing open datasets on said benchmarks. We conclude with analysis and discussion of training methods that did not reliably improve performance. The Tülu 3 release includes model weights, a demo, and the complete recipe — datasets for diverse core skills, a robust toolkit for data curation and evaluation, the training code and infrastructure, and, most importantly, a detailed report for reproducing and further adapting the Tülu 3 approach to more domains. https://arxiv.org/pdf/2411.15124 https://arxiv.org/pdf/2411.15124

by pizza 2y ago

anon373839 2y ago

Thanks for the input. It sounds like the task is about as daunting as it seems, then, but doable. Are there any resources (such as papers) you’ve found especially helpful?

anon373839 2y ago
To answer my own question in case anyone else has it: The Tülu 3 paper is really illuminating: > Language model post-training is applied to refine behaviors and unlock new skills across a wide range of language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for post-training are simultaneously the most important pieces of the puzzle and the portion with the least transparency. To bridge this gap, we introduce Tülu 3, a family of fully-open state-of-the-art post-trained models, alongside its data, code, and training recipes, serving as a comprehensive guide for modern post-training techniques. Tülu 3, which builds on Llama 3.1 base models, achieves results surpassing the instruct versions of Llama 3.1, Qwen 2.5, Mistral, and even closed models such as GPT-4o-mini and Claude 3.5-Haiku. The training algorithms for our models include supervised finetuning (SFT), Direct Preference Optimization (DPO), and a novel method we call Reinforcement Learning with Verifiable Rewards (RLVR). With Tülu 3, we build a multi-task evaluation scheme for post-training with development and unseen evaluations, standard benchmark implementations, and substantial decontamination of existing open datasets on said benchmarks. We conclude with analysis and discussion of training methods that did not reliably improve performance. The Tülu 3 release includes model weights, a demo, and the complete recipe — datasets for diverse core skills, a robust toolkit for data curation and evaluation, the training code and infrastructure, and, most importantly, a detailed report for reproducing and further adapting the Tülu 3 approach to more domains. https://arxiv.org/pdf/2411.15124 https://arxiv.org/pdf/2411.15124