| --- |
| license: gpl-2.0 |
| language: |
| - en |
| tags: |
| - scaling_laws |
| - pre_training |
| - llms |
| - language_modeling |
| - llama |
| paper: >- |
| Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law |
| Extrapolation |
| venue: NeurIPS 2026 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: '**/*.json' |
| dataset_info: |
| features: |
| - name: checkpoint_prefix |
| dtype: string |
| - name: timestamp |
| dtype: string |
| - name: status |
| dtype: string |
| - name: model_size |
| dtype: int64 |
| - name: d_model |
| dtype: int64 |
| - name: n_layers |
| dtype: int64 |
| - name: n_heads |
| dtype: int64 |
| - name: d_ff |
| dtype: int64 |
| - name: vocab_size |
| dtype: int64 |
| - name: max_seq_len |
| dtype: int64 |
| - name: learning_rate |
| dtype: float64 |
| - name: epochs |
| dtype: int64 |
| - name: batch_size |
| dtype: int64 |
| - name: weight_decay |
| dtype: float64 |
| - name: seed |
| dtype: int64 |
| - name: deterministic |
| dtype: bool |
| - name: cudnn_deterministic |
| dtype: bool |
| - name: cudnn_benchmark |
| dtype: bool |
| - name: total_tokens |
| dtype: int64 |
| - name: total_steps |
| dtype: int64 |
| - name: cumulative_time_seconds |
| dtype: float64 |
| - name: val_epochs_count |
| dtype: int64 |
| - name: mean_val_loss |
| dtype: float64 |
| - name: best_val_loss |
| dtype: float64 |
| - name: final_train_loss |
| dtype: float64 |
| - name: std_train_loss |
| dtype: float64 |
| - name: mean_train_loss |
| dtype: float64 |
| - name: global_batch_tokens |
| dtype: int64 |
| - name: final_val_loss |
| dtype: float64 |
| --- |
| # Collinear/Non-Collinear Scaling Models |
| Checkpoint repository for scaling law experiments comparing **collinear (CO)** and **non-collinear (NC)** experimental designs for the paper *Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation* under review for NeurIPS 2026. |
|
|
| ## Code |
| Anonymized code repository (reproduces all tables): [anonymous.4open.science](https://anonymous.4open.science/r/Tokens-per-Parameter_Coverage_Is_Critical_for_Robust_LLM_Scaling_Law_Extrapolation-CC76) |
|
|
| ## Directory Structure |
| `{dataset}/{design}/N_{param_count}/` |
| - **Dataset**: `wikipedia`, `pes2o`, `cosmopedia`, `redpajama`, `c4` (plus `_bf16` and `_bigtpp` variants) |
| - **Design**: `colinear` or `non_colinear` |
| - **N**: Model parameter count (one of 14 canonical sizes from ~5M to ~76.5M) |
| ## Experimental Designs |
| **Collinear (CO):** Models are trained along a line in (N, D) space where D = TPP × N for varying TPP (tokens per parameter) values. A single model size N is swept across many TPP values. |
|
|
| **Non-collinear (NC):** Models are trained on a grid over (N, D) space (`NxD_GRID`), varying both N and D independently. |
| ## Holdout Sets |
| Some checkpoints include `HOLDOUT` in the filename. These were **held out from scaling law fitting** and are used to evaluate extrapolation / interpolation accuracy of fitted scaling laws. Both CO and NC designs have holdout checkpoints: |
| - `COLINEAR_HOLDOUT_*` → collinear holdout (held-out TPP values) |
| - `*_HOLDOUT_*` (without `COLINEAR`) → non-collinear holdout (held-out (N, D) pairs) |
| ## Filename Convention |
| `{PREFIX}{DESIGN}N{approx_size}[TPP{val}]D{tokens}_{dataset}_m{exact_N}_token{exact_D}lr{lr}..._completedAt{timestamp}.pt` |
|
|
| The `m{N}` and `token{D}` fields contain the exact parameter count and token count used for training. |
|
|
| Full results pre-extracted are available in this repository aswell, under: extracted_losses/ , full_coverage_results/, prelim_bigtpp_results/, seed_variance_results/, special_results/ and subset_bbox_seeds/ . |
|
|
| --- |
| License: GPL v2.0 |
