We lifted 9 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| thu-yao-01-luo/multipowerlaw | canonical | 7 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| const_lrs | Ran | thu-yao-01-luo/multipowerlaw/src/lr_schedules.py code served (permissive licence) · get_code("0f6305535337d38c") |
| cosine_lrs | Ran | thu-yao-01-luo/multipowerlaw/src/lr_schedules.py code served (permissive licence) · get_code("00660cbc28acb453") |
| generate_init_params | Ran | thu-yao-01-luo/multipowerlaw/src/fitting.py code served (permissive licence) · get_code("cd6f7ee1b324e473") |
| huber_loss | Ran | thu-yao-01-luo/multipowerlaw/src/utils.py code served (permissive licence) · get_code("72abd9f687e8eaaa") |
| preprocess_data | Ran | thu-yao-01-luo/multipowerlaw/src/utils.py code served (permissive licence) · get_code("2e1fc14a90913b68") |
| torch_huber | Ran | thu-yao-01-luo/multipowerlaw/src/utils.py code served (permissive licence) · get_code("68cfdcc1f7b3559f") |
| two_stage_lrs | Ran | thu-yao-01-luo/multipowerlaw/src/lr_schedules.py code served (permissive licence) · get_code("bf5b813192b4d050") |
| initialize_params | Not yet run | thu-yao-01-luo/multipowerlaw/src/fitting.py code served (permissive licence) · get_code("f787cc931f766988") |
| load_data | Not yet run | thu-yao-01-luo/multipowerlaw/src/data_loader.py code served (permissive licence) · get_code("57b2ae61ea02dcf2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Training large models is both resource-intensive and time-consuming, making it crucial to understand the quantitative relationship between model performance and hyperparameters. In this paper, we present an empirical law that describes how the pretraining loss of large language models evolves under different learning rate schedules, such as constant, cosine, and step decay schedules. Our proposed law takes a multi-power form, combining a power law based on the sum of learning rates and additional power laws to account for a loss reduction effect induced by learning rate decay. We extensively validate this law on various model sizes and architectures, and demonstrate that after fitting on a few learning rate schedules, the law accurately predicts the loss curves for unseen schedules of different shapes and horizons. Moreover, by minimizing the predicted final pretraining loss across learning rate schedules, we are able to find a schedule that outperforms the widely used cosine learning rate schedule. Interestingly, this automatically discovered schedule bears some resemblance to the recently proposed Warmup-Stable-Decay (WSD) schedule (Hu et al, 2024) but achieves a slightly lower final loss. We believe these results could offer valuable insights for understanding the dynamics of pretraining and designing learning rate schedules to improve efficiency.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.12811")
get_code_for_paper("2503.12811")
have("2503.12811")
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