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Reading the TÜLU 3 paper from @ai2.bsky.social. It's refreshing to see a research lab treating AI as a real science with full reports, data, code, logs, evals. Paper: allenai.org/papers/tulu-... Demo: playground.allenai.org Code: github.com/allenai/open... Eval: github.com/allenai/olmes Notes

Core skills - they identify some key areas open post-training models struggle with and target those for improving, picking datasets in those areas for evaluation. Notably, they avoid looking at scores from the unseen set during development to assess if their choices might have led to overfitting.

Data sources - they curate from public datasets based on three criteria - diversity (for generalization), targeting their identified core skills and clear licensing/provenance for proper usage. Datasets they considered: docs.google.com/spreadsheets...

Data sources - they combat mode collapse using persona-driven synthetic data generation, where unique personas (e.g., "ML researcher focused on neural networks") guide LLMs to create diverse training data for specific skills like precise instruction following, math, and coding.

Data decontamination - n-gram matching works better than embedding methods in terms of precision - 8-gram matching only on user-turns (since completions are LLM generated) - remove training sets that have more than 2% overlap with any evaluation set

SFT - for the data, train models on skill specific data mixtures, keep the mixtures that lead to the best performance on those skills. Later, combine the mixtures and do decontamination/downsampling of larger datasets.

SFT - a couple of data ablations: - Adding safety data didn't negatively affect other skills. - Measured the removal of math data on GSM8K and MATH, confirming that the scores drop. - Measured the the effectiveness of WildChat (diverse chat data) and Persona (skill based data)

SFT - performance varies greatly depending on the random seed. They tried model soups (averaging weights of multiple models using github.com/arcee-ai/mer...), but decided to choose a single best model instead.

DPO - preference data is made of a on-policy model pool (Tulu 3 7B/70B) and off-policy model pool (other models). GPT-4o is used to rate generations from 4 random models from the model pool from 1-5. The highest is taken as the chosen response and the rejected response is sampled from the rest.

It's interesting how well LLM-as-a-judge worked for them here, it's completely viable now to evaluate your data using LLMs (even for seemingly fuzzy tasks like preference). related: aligned.substack.com/p/crisp-and-...