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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 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)