Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

1. 1
   Set Up Base LLM Architecture

   Start with a standard Transformer-based decoder-only LLM. Ensure you have access to the model's hidden states (latent representations) at each token position. This example uses a simplified PyTorch-like structure for illustration.
2. 2
   Implement Multi-Token Prediction (MTP) Loss

   Modify the standard Next-Token Prediction (NTP) loss to calculate cross-entropy over `k` future tokens. Sum the losses for each of these `k` predictions. This encourages the model to consider a broader future context.
3. 3
   Design Latent Semantic Enhancement

   Add a mechanism to encourage semantic consistency in the latent space. One approach is to use a 'semantic consistency loss' by comparing the latent representation of a predicted sequence with a target semantic representation. This can involve an auxiliary encoder or a pre-trained semantic space.
4. 4
   Combine Losses and Train

   Integrate the MTP loss and the Latent Semantic Enhancement loss into a combined objective. Define a weighting factor (lambda) for the semantic loss. Then, perform standard LLM training using this combined loss function.

import torch
import torch.nn as nn

def calculate_mtp_loss_starter(logits, targets, k=3):
    # Simplified MTP loss calculation for demonstration.
    # Assumes logits are (batch_size, seq_len, vocab_size)
    # and targets are (batch_size, seq_len).
    # This calculates loss for predicting targets[i+1]...targets[i+k] based on input up to i.
    
    batch_size, seq_len, vocab_size = logits.shape
    total_loss = 0.0
    num_predictions = 0
    
    for i in range(seq_len - 1):
        for j in range(1, k + 1):
            if i + j < seq_len:
                # Logits at position 'i' are used to predict tokens from 'i+1' onwards.
                # The cross_entropy function expects (N, C) and (N) for inputs and targets.
                pred_logits = logits[:, i, :]
                actual_target = targets[:, i+j]
                
                loss = nn.functional.cross_entropy(pred_logits, actual_target, reduction='mean')
                total_loss += loss
                num_predictions += 1
                
    return total_loss / max(1, num_predictions) # Avoid division by zero

# Example usage:
vocab_size = 1000
seq_len = 10
batch_size = 2
k_value = 2

# Simulate model output (logits) and target tokens
sim_logits = torch.randn(batch_size, seq_len, vocab_size)
sim_targets = torch.randint(0, vocab_size, (batch_size, seq_len))

mtp_loss_result = calculate_mtp_loss_starter(sim_logits, sim_targets, k=k_value)
print(f"Calculated MTP Loss (k={k_value}): {mtp_loss_result.item():.4f}")