---
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