Boost language model performance by strategically ordering training data without changing content or model size. Uses learnability-quality scoring and folding schedules to improve convergence and knowledge retention, achieving consistent gains across all model scales.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Boost language model performance by strategically ordering training data without changing content or model size. Uses learnability-quality scoring and folding schedules to improve convergence and knowledge retention, achieving consistent gains across all model scales.
Data Efficacy: Organizing Training Data for Better Learning
Training data order matters far more than commonly assumed. Models trained on shuffled data waste capacity on redundant examples early in training. Yet most practitioners treat data ordering as a minor implementation detail. DELT (Data Efficacy for Language Model Training) shows that systematic data organization—without any other changes—consistently improves performance across model sizes and domains.
The key insight: selecting good training samples is important, but organizing them strategically is equally powerful. Curriculum learning shows promise, but simple approaches like sorting once causes distribution bias. DELT introduces Learnability-Quality Scoring (measuring which samples reduce loss most) and Folding Ordering (intelligently repeating sorted batches at intervals) to combine the benefits of both approaches while avoiding pitfalls.
Core Concept
DELT separates data handling into three independent stages:
Data Scoring: Measure each sample's "learnability"—how much gradient signal it provides and how aligned it is with overall training objectives
Data Selection: Optionally filter low-quality samples (can be combined with scoring)
Data Ordering: Reorganize training sequences using folding patterns that prevent distribution bias
The insight is that learnability isn't static. Early in training, simple examples provide useful learning signals. Later, harder examples push the model to generalization. Folding ordering repeats sorted data multiple times—interleaving easy and hard examples—to maintain balanced curriculum while avoiding catastrophic forgetting.
Architecture Overview
The DELT pipeline consists of these components:
Learnability-Quality Scorer: Evaluates samples by analyzing gradient consistency—both magnitude (how much learning signal) and direction (alignment with training objectives)
Folding Ordering Scheduler: Takes sorted data and reorganizes into multiple "folds" that interleave difficulty levels across epochs
Baseline Metrics: Measures on math, code, and general language tasks to ensure robustness
Multi-Scale Validation: Tests across 160M to 1B parameter models to confirm scalability
The design avoids common pitfalls: simple curriculum learning (monotonic difficulty causes overfitting), random shuffling (wastes early training capacity), and static orderings (doesn't adapt as the model learns).
Implementation
This section demonstrates how to implement data efficacy in language model training.
Step 1: Calculate learnability-quality scores for each sample
This code computes how much each sample contributes to learning and alignment:
import numpy as np
import torch
from torch.utils.data import Dataset
defcompute_learnability_quality_scores(dataset, model, loss_fn, batch_size=32, num_samples=None):
"""
Compute LQS (Learnability-Quality Score) for each sample.
LQS measures gradient consistency: both magnitude and alignment with objectives.
"""
model.eval()
scores = []
sample_indices = np.random.choice(len(dataset), size=min(num_samples orlen(dataset), 1000))
for idx in sample_indices:
sample = dataset[idx]
input_ids = sample['input_ids'].unsqueeze(0).to(model.device)
target_ids = sample['target_ids'].unsqueeze(0).to(model.device)
# Compute gradient with respect to this sample
input_ids.requires_grad_(True)
logits = model(input_ids).logits
loss = loss_fn(logits.view(-1, model.config.vocab_size), target_ids.view(-1))
# Backprop to get gradients
loss.backward()
gradients = input_ids.grad
# Learnability: magnitude of gradient (how much this sample moves model)
gradient_magnitude = (gradients ** 2).sum().sqrt().item()
# Quality: alignment with overall training direction (consistency)# Sample that reduces loss consistently across small perturbations has high quality
quality = compute_gradient_consistency(model, input_ids, target_ids, loss_fn)
# Combined score: magnitude × alignment
lqs = gradient_magnitude * quality
scores.append({
'index': idx,
'magnitude': gradient_magnitude,
'quality': quality,
'lqs': lqs
})
return scores
defcompute_gradient_consistency(model, input_ids, target_ids, loss_fn, num_perturbs=5):
"""
Measure gradient consistency: does this sample provide stable learning signal?
"""
base_loss = model(input_ids).logits
base_loss = loss_fn(base_loss.view(-1, model.config.vocab_size), target_ids.view(-1))
consistency_scores = []
for _ inrange(num_perturbs):
# Small random perturbation to model weightswith torch.no_grad():
for param in model.parameters():
if param.requires_grad:
param.add_(torch.randn_like(param) * 1e-4)
perturbed_loss = model(input_ids).logits
perturbed_loss = loss_fn(perturbed_loss.view(-1, model.config.vocab_size), target_ids.view(-1))
# Consistency: does loss reduction remain stable?
consistency = 1.0 / (1.0 + abs(perturbed_loss.item() - base_loss.item()))
consistency_scores.append(consistency)
# Revert perturbationwith torch.no_grad():
for param in model.parameters():
if param.requires_grad:
param.add_(torch.randn_like(param) * -1e-4)
return np.mean(consistency_scores)
# Score all training samples
train_dataset = load_language_model_dataset()
lqs_scores = compute_learnability_quality_scores(train_dataset, model, loss_fn)
# Sort by LQS
sorted_scores = sorted(lqs_scores, key=lambda x: x['lqs'], reverse=True)
print(f"Top-scored sample LQS: {sorted_scores[0]['lqs']:.4f}")
print(f"Bottom-scored sample LQS: {sorted_scores[-1]['lqs']:.4f}")
This measures which training samples provide stable, high-magnitude learning signals.
Step 2: Apply folding ordering to create curriculum without distribution bias
This code reorganizes sorted data into multiple folds that interleave difficulty:
import math
defcreate_folding_ordering(scored_samples, num_folds=5):
"""
Folding Ordering (FO): repeat sorted data multiple times with interleaving.
This maintains curriculum while preventing overfitting to easy examples.
"""# Sort by LQS score
sorted_indices = [s['index'] for s insorted(scored_samples, key=lambda x: x['lqs'], reverse=True)]
num_samples = len(sorted_indices)
fold_size = math.ceil(num_samples / num_folds)
# Create folds: [easy, medium-easy, medium, medium-hard, hard] × 5 epochs
folded_order = []
for epoch inrange(num_folds):
# This epoch: sample from each difficulty level in round-robinfor fold_id inrange(num_folds):
start_idx = fold_id * fold_size
end_idx = min((fold_id + 1) * fold_size, num_samples)
# Add samples from this fold for this epoch
fold_samples = sorted_indices[start_idx:end_idx]
folded_order.extend(fold_samples)
return folded_order
# Create curriculum schedule
folding_order = create_folding_ordering(lqs_scores, num_folds=5)
# Visualize difficulty distribution across epochs
epoch_size = len(lqs_scores)
for epoch inrange(5):
epoch_samples = folding_order[epoch * epoch_size:(epoch + 1) * epoch_size]
avg_lqs = np.mean([lqs_scores[i]['lqs'] for i in epoch_samples])
print(f"Epoch {epoch}: average LQS = {avg_lqs:.4f}")
This creates a curriculum where each epoch interleaves easy and hard examples, preventing both shortcut learning and forgetting.
Step 3: Train with data efficacy without modifying model architecture
This shows how to use the ordering in standard training loops:
import torch
from torch.utils.data import Sampler
classFoldingOrderSampler(Sampler):
"""Custom sampler that enforces folding order without modifying model."""def__init__(self, folding_order):
self.folding_order = folding_order
def__iter__(self):
returniter(self.folding_order)
def__len__(self):
returnlen(self.folding_order)
# Create data loader with folding order
train_sampler = FoldingOrderSampler(folding_order)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=32,
sampler=train_sampler,
num_workers=4
)
# Standard training loop—no model changes needed
model = AutoModelForCausalLM.from_pretrained("gpt2")
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
for epoch inrange(5):
for batch_idx, batch inenumerate(train_loader):
input_ids = batch['input_ids'].to(model.device)
attention_mask = batch['attention_mask'].to(model.device)
outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
if batch_idx % 100 == 0:
print(f"Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item():.4f}")
print("Training complete with data efficacy—no architecture changes!")
This integrates folding ordering into standard PyTorch training without any model modifications.
Step 4: Measure improvements across benchmarks
This code evaluates performance gains on diverse tasks:
from datasets import load_dataset
defevaluate_on_benchmarks(model, test_datasets=['math', 'code', 'general']):
"""
Evaluate model on representative tasks from math, code, and general domains.
DELT shows consistent improvements across all without tuning per-domain.
"""
results = {}
# Math benchmark (arithmetic reasoning)if'math'in test_datasets:
math_data = load_dataset("math_qa", split="test")
math_acc = evaluate_qa_accuracy(model, math_data)
results['math_accuracy'] = math_acc
print(f"Math QA Accuracy: {math_acc:.2%}")
# Code benchmark (program synthesis)if'code'in test_datasets:
code_data = load_dataset("human_eval", split="test")
code_pass = evaluate_pass_at_k(model, code_data, k=1)
results['code_pass_1'] = code_pass
print(f"Code Pass@1: {code_pass:.2%}")
# General language understandingif'general'in test_datasets:
general_data = load_dataset("arc", split="test")
general_acc = evaluate_qa_accuracy(model, general_data)
results['general_accuracy'] = general_acc
print(f"General QA Accuracy: {general_acc:.2%}")
return results
defevaluate_qa_accuracy(model, dataset, max_samples=1000):
"""Helper: compute accuracy on QA tasks."""
correct = 0for sample in dataset.select(range(min(len(dataset), max_samples))):
question = sample.get('question', sample.get('text', ''))
predicted = model.generate(question, max_length=50)
reference = sample.get('answer', sample.get('label', ''))
if predicted.strip().lower() in reference.lower():
correct += 1return correct / min(len(dataset), max_samples)
defevaluate_pass_at_k(model, dataset, k=1, max_samples=164):
"""Helper: compute pass@k on code generation."""
passes = 0for sample in dataset.select(range(min(len(dataset), max_samples))):
prompt = sample['prompt']
solutions = [model.generate(prompt) for _ inrange(k)]
# Check if any solution passes (simplified; real evaluation uses execution)ifany("def "in sol for sol in solutions):
passes += 1return passes / min(len(dataset), max_samples)
# Evaluate
benchmarks = evaluate_on_benchmarks(model, test_datasets=['math', 'code', 'general'])
This measures improvements across diverse benchmarks to verify robustness of DELT gains.
Practical Guidance
When to use Data Efficacy:
Training language models from scratch with limited computational budget
Fine-tuning on small to medium datasets where every sample matters
Tasks where sample quality varies widely (curriculum learning naturally helps)
Scenarios where you want improvements without architectural changes or additional compute
When NOT to use:
Web-scale training with massive, well-curated datasets (diminishing returns)
Real-time applications where preprocessing overhead is critical
Cases where all samples are already well-balanced in quality
Scenarios requiring model-specific optimizations (data ordering is general)
Hyperparameters and Configuration:
Parameter
Recommended
Notes
Number of Folds
5
Balances curriculum structure with epoch variation
Gradient Samples for Scoring
1000-5000
Estimate LQS on subset to avoid full-dataset cost
Gradient Consistency Perturbations
5
Sufficient to assess stability; more → slower scoring
Model Sizes
160M-1B
DELT benefits observed across all scales
Epochs
4-10
More epochs leverage ordering; diminishing returns after 10
Batch Size
32-128
Independent of DELT; use standard guidance
Common Pitfalls:
Scoring the entire dataset before training (expensive; sample 1000-5000 instead)
Using simple loss magnitude as the score (ignore consistency—leads to outliers)
Applying one fold order for all epochs (reduces diversity; regenerate each epoch)
Combining DELT with other reordering schemes (incompatible assumptions)
Assuming LQS captures absolute quality (it's relative; lower-scored samples still help after easier ones are learned)
Key Design Decisions:
DELT doesn't modify the model or training algorithm—only data order. This makes it immediately applicable to any training framework. Folding ordering avoids monotonic curriculum (which causes overfitting) by repeating sorted data across epochs with round-robin interleaving. Learnability-Quality Scoring combines gradient magnitude (learning signal strength) and consistency (stability), avoiding both noisy samples and outlier examples that reduce loss but don't generalize.
Reference
Liu, Y., Wang, H., Ye, J., Chen, T., Zhang, Y., & Yang, Y. (2025). Data Efficacy for Language Model Training. arXiv preprint arXiv:2506.21545. https://arxiv.org/abs/2506.21545
