Request for a model training tutorial #3

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1. linggm3 commented on Oct 19, 2025

   

   linggm3

   on Oct 19, 2025

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   Hi~
   Thanks for your interest! The llm_performance.npz file contains preprocessed data for training and evaluating the MLP router. Specifically, it includes six arrays:

   • train_embed, val_embed, test_embed: embeddings for train/val/test splits.
   • train_score, val_score, test_score: corresponding performance scores.

   For reference, test_router.py outlines the pipeline to build the .npz dataset and perform router training and testing.

   Here’s the relevant code snippet:

   np.savez('data/tmp_perf.npz', 
            train_embed=router_dataset['embedding']['train_embed'],
            val_embed=router_dataset['embedding']['val_embed'],
            test_embed=router_dataset['embedding']['test_embed'],
            train_score=train_score, 
            val_score=val_score, 
            test_score=test_score)

   You can simply change baseline = 'knn' to baseline = 'mlp' in test_router.py and run python test_router.py to train and test the MLP router.

   Let me know if you need more details! 😊
2. TonyZhang-sjtu commented on Oct 19, 2025

   

   TonyZhang-sjtu

   on Oct 19, 2025

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   Big thanks for your timely and comprehensive response. I've run the code successfully and get more questions that I'd like to discuss with you~

   1. I noticed a line if difficulty == 'hard': break, which seems to exclude the experiments with 10, 100 and 1000 LLM candidates. How can I reproduce the experiment under "hard" setting?

   2. The appendix of the paper said, "For specific candidates of these settings and other evaluation candidates, please refer directly to our code." But I didn't find the definition of these candidates and code of choosing the most proper candidates for "all-strong", "all-weak" and "strong-to-weak" settings. Would you please show me how I can get to know and access the specific LLM candidates?

   3. I also wanna try using different LLM candidates like 3, 6, or 20 etc. But I find the definition seems to be imported directly from data/router_dataset/{to_handle}_router_dataset.pkl. Therefore, how can I modify or regenerate these pkl files?

   4. Under the leaderboard_embed, leaderboard_prompt, and leaderboard_score folders, there are pkl files named _mmlu_pro, _new, and _old. What are the differences between them? When I try to train a router, which one should I refer to?

   Thank u very much!!!
3. linggm3 commented on Oct 19, 2025

   

   linggm3

   on Oct 19, 2025

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   Thanks for your questions! Here are quick answers:

   1. Simply remove or comment out the line if difficulty == 'hard': break to run the "hard" setting experiments.

   2. Check "Advanced Tutorial 2: Constructing Router Dataset" in the README and the get_router_dataset.py file for candidate definitions and selection methods.

   3. Modify get_router_dataset.py to generate custom PKL files with your desired LLM candidates (3, 6, 20, etc.).

   4. They denote different benchmark sets. The differences are:

   • _old: 6 earlier benchmarks (generally higher LLM scores)
   • _new/_mmlu_pro: Newer, more challenging benchmarks (lower scores)

   We recommend training and testing your router across all of them for a comprehensive evaluation.

   Hope this helps! Feel free to reach out if you have more questions.