Scale LLM training using RL on unlabeled pre-training corpora without human annotation. Derive reward signals directly from text segments to optimize both autoregressive generation and in-context reasoning across knowledge and mathematical domains.
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Scale LLM training using RL on unlabeled pre-training corpora without human annotation. Derive reward signals directly from text segments to optimize both autoregressive generation and in-context reasoning across knowledge and mathematical domains.
Outcome: Scaling LLM Capability Beyond Supervised Training Data Limits
Enable language models to improve reasoning and knowledge through reinforcement learning signals derived directly from unlabeled pre-training data, eliminating dependence on human feedback while maintaining favorable scaling behavior across general knowledge and mathematical reasoning benchmarks.
Problem Context
Large language models face a fundamental scaling constraint: exponential computational growth requires quadratic growth in high-quality text data, but the supply of human-curated, annotated data remains finite. Methods like RLHF and RLVR depend on human labeling, which introduces bottlenecks in data throughput and scale.
Existing RL approaches require reward models trained on explicit human feedback, limiting scalability to problem domains where obtaining such annotations is expensive or impractical. This creates a hard ceiling on training efficiency: more compute doesn't automatically translate to better performance when reward signal generation becomes the constraint.
Core Concept
RLPT (Reinforcement Learning on Pre-Training data) derives reward signals directly from existing unlabeled text corpora instead of human annotation. The key insight is that accurate prediction of subsequent text segments—either the next continuation or masked intermediate text—serves as a meaningful learning signal for improving reasoning and generalization.
The method constructs two complementary RL objectives from raw web text:
Autoregressive Segment Reasoning (ASR): The model predicts the next logical text segment given preceding context. Correct prediction earns reward; this trains autoregressive generation quality and forward-looking reasoning.
Middle Segment Reasoning (MSR): The model predicts masked or removed text spans using bidirectional context. This trains the model to leverage surrounding information and develop more robust semantic understanding.
Both tasks exploit the self-contained structure of pre-training corpora as an unlimited source of reward signals, enabling continuous RL training without external annotation.
Architecture Overview
Data Foundation: Aggregated web text from Wikipedia, arXiv, conversation threads, and general web corpora with MinHash-based deduplication and PII masking
Reward Mechanism: Generative reward model evaluates semantic consistency between policy predictions and ground-truth text segments
Dual Task Structure: ASR (forward prediction) and MSR (bidirectional reconstruction) objectives simultaneously optimize generation and understanding
Policy: Base language model (e.g., Qwen3-4B-Base) trained via policy gradient methods using the derived reward signal
Scaling: Training operates on the same pre-training data scale without bottlenecks from annotation pipelines
Implementation
Step 1: Prepare Pre-Training Corpus with Segment Extraction
The foundation is a large, deduplicated text corpus. Extract training examples by segmenting text into logical chunks and creating segment prediction tasks.
# segment_extraction.pyimport hashlib
from collections import defaultdict
classSegmentCorpus:
def__init__(self, min_segment_length=64, max_segment_length=256):
self.min_length = min_segment_length
self.max_length = max_segment_length
self.hash_cache = defaultdict(list)
defdeduplicate_minihash(self, documents, num_hashes=128):
"""Remove duplicate documents using MinHash fingerprinting."""defget_shingles(text, k=4):
return [text[i:i+k] for i inrange(len(text) - k + 1)]
unique_docs = []
for doc in documents:
shingles = get_shingles(doc)
hashes = [hash(s) % (2**32) for s in shingles[:num_hashes]]
fingerprint = tuple(sorted(hashes))
if fingerprint notinself.hash_cache:
self.hash_cache[fingerprint].append(doc)
unique_docs.append(doc)
return unique_docs
defextract_segments(self, document, stride=None):
"""Break document into logical segments for training."""
sentences = document.split('. ')
segments = []
current_segment = ""for sentence in sentences:
iflen(current_segment) + len(sentence) < self.max_segment_length:
current_segment += sentence + ". "else:
iflen(current_segment) >= self.min_length:
segments.append(current_segment.strip())
current_segment = sentence + ". "iflen(current_segment) >= self.min_length:
segments.append(current_segment.strip())
return segments
defcreate_training_pairs(self, document):
"""Create (context, target_segment) pairs for ASR and MSR tasks."""
segments = self.extract_segments(document)
training_pairs = []
# Autoregressive Segment Reasoning (ASR)for i inrange(len(segments) - 1):
context = " ".join(segments[:i+1])
target = segments[i+1]
training_pairs.append(("ASR", context, target))
# Middle Segment Reasoning (MSR): mask middle segmentfor i inrange(1, len(segments) - 1):
before = " ".join(segments[:i])
after = " ".join(segments[i+1:])
target = segments[i]
training_pairs.append(("MSR", (before, after), target))
return training_pairs
corpus = SegmentCorpus()
documents = [...] # Load pre-training documents
unique_docs = corpus.deduplicate_minihash(documents)
Step 2: Initialize Reward Model Using Semantic Consistency
Create a reward model that evaluates whether the policy's predicted segment is semantically consistent with ground-truth continuation.
# reward_model.pyimport torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
classSemanticRewardModel:
def__init__(self, base_model_name="bert-base-uncased", device="cuda"):
self.tokenizer = AutoTokenizer.from_pretrained(base_model_name)
self.encoder = AutoModel.from_pretrained(base_model_name).to(device)
self.device = device
defget_embeddings(self, texts):
"""Encode text to dense embeddings."""
inputs = self.tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.encoder(**inputs)
embeddings = outputs.last_hidden_state[:, 0, :] # [CLS] tokenreturn F.normalize(embeddings, p=2, dim=1)
defcompute_reward(self, predicted_segment, ground_truth_segment):
"""Compute reward as cosine similarity between embeddings."""
pred_emb = self.get_embeddings([predicted_segment])
true_emb = self.get_embeddings([ground_truth_segment])
# Cosine similarity: higher is better
similarity = F.cosine_similarity(pred_emb, true_emb)
# Scale to [0, 1] range and apply margin
reward = (similarity + 1) / 2# Map [-1, 1] to [0, 1]return reward.item()
defbatch_compute_rewards(self, predicted_segments, ground_truth_segments):
"""Compute rewards for a batch of predictions."""
pred_embs = self.get_embeddings(predicted_segments)
true_embs = self.get_embeddings(ground_truth_segments)
rewards = F.cosine_similarity(pred_embs, true_embs)
rewards = (rewards + 1) / 2# Normalize to [0, 1]return rewards.detach().cpu().numpy()
reward_model = SemanticRewardModel()
reward = reward_model.compute_reward(
predicted="The capital of France is Paris",
ground_truth="The capital of France is Paris"
)
Step 3: Implement Policy Gradient Training Loop
Train the base language model using policy gradient methods with the derived reward signal.
# policy_training.pyimport torch
import torch.optim as optim
from transformers import AutoModelForCausalLM, AutoTokenizer
classRLPTTrainer:
def__init__(self, model_name, reward_model, learning_rate=5e-5, gamma=0.99):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.policy = AutoModelForCausalLM.from_pretrained(model_name)
self.reward_model = reward_model
self.optimizer = optim.Adam(self.policy.parameters(), lr=learning_rate)
self.gamma = gamma
self.device = "cuda"if torch.cuda.is_available() else"cpu"self.policy.to(self.device)
defgenerate_segment(self, context, max_length=128, temperature=0.7):
"""Generate candidate segment using the policy."""
input_ids = self.tokenizer.encode(context, return_tensors="pt").to(self.device)
with torch.no_grad():
outputs = self.policy.generate(
input_ids,
max_new_tokens=max_length,
temperature=temperature,
top_p=0.9,
do_sample=True,
return_dict_in_generate=True,
output_scores=True
)
generated_ids = outputs.sequences[0, input_ids.shape[1]:]
generated_text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
return generated_text
defcompute_policy_loss(self, context, generated_segment, ground_truth_segment, reward):
"""Compute policy gradient loss."""
input_ids = self.tokenizer.encode(context, return_tensors="pt").to(self.device)
target_ids = self.tokenizer.encode(generated_segment, return_tensors="pt").to(self.device)
# Forward pass through policy
outputs = self.policy(
input_ids=input_ids,
labels=target_ids
)
logits = outputs.logits
log_probs = torch.log_softmax(logits, dim=-1)
# Select log probabilities for generated tokens
generated_log_probs = log_probs[0, torch.arange(target_ids.shape[1]-1), target_ids[0, 1:]]
policy_loss = -(generated_log_probs.sum() * reward)
return policy_loss
deftrain_step(self, batch_data):
"""Single training step over a batch of examples."""
total_loss = 0for context, generated_seg, ground_truth_seg in batch_data:
# Get reward signal from reward model
reward = self.reward_model.compute_reward(generated_seg, ground_truth_seg)
# Compute policy gradient loss
loss = self.compute_policy_loss(context, generated_seg, ground_truth_seg, reward)
# Backward passself.optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), max_norm=1.0)
self.optimizer.step()
total_loss += loss.item()
return total_loss / len(batch_data)
deftrain_epoch(self, training_pairs, batch_size=8):
"""Train for one epoch over all training pairs."""self.policy.train()
total_loss = 0for i inrange(0, len(training_pairs), batch_size):
batch = training_pairs[i:i+batch_size]
batch_data = []
for task_type, context_or_tuple, ground_truth in batch:
if task_type == "ASR":
generated = self.generate_segment(context_or_tuple)
batch_data.append((context_or_tuple, generated, ground_truth))
elif task_type == "MSR":
before, after = context_or_tuple
context = before + " [MASK] " + after
generated = self.generate_segment(context)
batch_data.append((context, generated, ground_truth))
loss = self.train_step(batch_data)
total_loss += loss
return total_loss / (len(training_pairs) // batch_size)
trainer = RLPTTrainer("qwen2-4b", reward_model)
epoch_loss = trainer.train_epoch(training_pairs, batch_size=8)
Step 4: Evaluation on Downstream Benchmarks
Assess model improvements on standard benchmarks after RL training.
# evaluation.pyfrom datasets import load_dataset
import numpy as np
classBenchmarkEvaluator:
def__init__(self, model, tokenizer, device="cuda"):
self.model = model
self.tokenizer = tokenizer
self.device = device
self.model.eval()
defevaluate_mmlu(self, num_samples=None):
"""Evaluate on MMLU benchmark."""
dataset = load_dataset("cais/mmlu", "all")
test_data = dataset["test"]
if num_samples:
test_data = test_data.select(range(min(num_samples, len(test_data))))
correct = 0
total = 0for example in test_data:
question = example["question"]
choices = example["choices"]
answer_idx = example["answer"]
# Format multiple choice
prompt = f"{question}\nOptions: {', '.join(choices)}\nAnswer: "with torch.no_grad():
input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(self.device)
outputs = self.model.generate(input_ids, max_new_tokens=10)
response = self.tokenizer.decode(outputs[0])
# Simple check: does response contain correct answerif choices[answer_idx].lower() in response.lower():
correct += 1
total += 1
accuracy = correct / total if total > 0else0return {"mmlu_accuracy": accuracy, "correct": correct, "total": total}
defevaluate_aime(self, num_samples=None):
"""Evaluate on AIME-style math problems."""# Placeholder for AIME evaluation# In practice, would parse structured math problemsreturn {"aime_pass_at_1": 0.0}
defrun_all_benchmarks(self):
"""Run comprehensive evaluation."""
results = {}
results.update(self.evaluate_mmlu(num_samples=500))
results.update(self.evaluate_aime(num_samples=100))
return results
evaluator = BenchmarkEvaluator(trainer.policy, trainer.tokenizer)
benchmark_results = evaluator.run_all_benchmarks()
print(benchmark_results)
Practical Guidance
Hyperparameter Reference
Parameter
Recommended Value
Range
Effect
Learning Rate
5e-5
1e-5 to 1e-4
Lower = stable but slow; higher = faster convergence but less stable
Batch Size
8-16
4 to 64
Larger enables better gradient estimates; memory-limited by hardware
Higher values weight future rewards; 0.99 standard for RL
Generation Temperature
0.7
0.5-1.0
Lower = deterministic; higher = diverse exploration
When to Use RLPT
Abundant unlabeled data but scarce annotations: You have large pre-training corpora but cannot afford human feedback labeling
Scaling reasoning capabilities: Target improvements in knowledge recall, mathematical reasoning, or logical inference
Cost-sensitive RL training: Eliminate annotation bottlenecks that constrain throughput of other RL methods (RLHF, RLVR)
Domain-specific pre-training: Fine-tune existing models on domain corpora without external reward labels
Favorable scaling assumptions: You expect the model benefits from RL will compound with more compute and data
When NOT to Use RLPT
Preference-aligned training required: If the goal is safety, instruction-following, or aligning to human preferences, RLPT alone is insufficient. It optimizes for semantic consistency, not human judgment
Limited pre-training data: If your corpus is small or highly curated, the self-derived reward signal becomes weak and less reliable
Real-time or online learning: RLPT assumes static pre-training data; not designed for continual online updates with new user interactions
Few-shot or task-specific RL: When you have labeled data for specific tasks, supervised fine-tuning or supervised RL (like RLHF) will be more sample-efficient
Inference speed critical: The method adds training overhead; if model latency is the bottleneck, focus on quantization or distillation instead
Common Pitfalls
Stale reward signals: If the reward model is not updated as the policy improves, rewards become less meaningful. Periodically retrain or refresh the reward model on new policy samples.
Reward hacking: The policy may exploit quirks in the semantic similarity metric (e.g., copying ground-truth text verbatim). Use diverse reward models or regularize for output diversity.
Data contamination: If pre-training data contains benchmark test sets, RL training on that data inflates evaluation metrics. Carefully filter benchmark data before RL training.
Segment length sensitivity: Segments that are too short lose context; too long make training inefficient. Empirically tune segment extraction to your domain.
Unstable policy gradients: Large reward signals combined with high learning rates cause oscillation. Use gradient clipping, lower learning rates, and reward normalization.
Reference
Paper: Reinforcement Learning on Pre-Training Data (arXiv 2509.19249)
