ral-reinforced-attention-learning

name	ral-reinforced-attention-learning
title	Reinforced Attention Learning
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2602.04884
keywords	["Multimodal Learning","Attention Optimization","Policy Gradient","Perception","Vision-Language Models"]
description	Optimize where multimodal models attend by treating attention weights as a learnable policy, using policy gradients with advantage weighting to improve visual grounding and perception without changing model architecture.

Reinforced Attention Learning

Problem Context

Standard reinforcement learning for multimodal LLMs focuses on optimizing token-level outputs, which proves ineffective for perception-heavy tasks. Models need better mechanisms for allocating computational focus across visual and textual inputs. Current approaches don't improve visual grounding because text-based reward signals don't directly guide where attention should focus. The model may generate correct answers despite poor visual attention.

Core Concept

RAL reformulates post-training to optimize [internal attention distributions, advantage weighting, policy gradient] as the primary objective. Rather than only improving what the model generates, RAL improves where the model attends. Attention weights are treated as a policy that governs information selection, optimized via policy gradients weighted by task advantages.

Architecture Overview

• Attention as policy: Extract attention weights from transformer layers; treat as learnable policy
• Advantage-weighted loss: Use Jensen-Shannon Divergence between current and reference attention, weighted by advantage signals
• Dual optimization: Combine standard token-level gradients with attention-level supervision
• On-policy distillation: Transfer attention patterns from teacher to student models
• Layer targeting: Apply selectively to layers most relevant for perception (middle-to-later layers)

Implementation

Step 1: Extract and profile attention patterns

Extract attention weights from model during forward pass. Profile which layers are most relevant for perception.

# Extract attention patterns
class AttentionExtractor:
    def __init__(self, model, layer_indices=None):
        self.model = model
        if layer_indices is None:
            # Default: middle to later layers for perception
            num_layers = model.config.num_hidden_layers
            self.layer_indices = list(
                range(num_layers // 2, num_layers)
            )
        :
            .layer_indices = layer_indices

     ():
        
         torch.no_grad():
            outputs = .model(
                input_ids=input_ids,
                pixel_values=pixel_values,
                output_attentions=,
                return_dict=
            )

        attention_weights = {}
         layer_idx  .layer_indices:
            
            attn = outputs.attentions[layer_idx]
            
            attn_mean = attn.mean(dim=(, ))  
            attention_weights[layer_idx] = attn_mean

         attention_weights

     ():
        
        focus_scores = {}

         layer_idx, attn_matrix  attention_weights.items():
            
            text_start = num_visual_tokens
            visual_to_text_attn = attn_matrix[text_start:, :num_visual_tokens].mean()
            focus_scores[layer_idx] = visual_to_text_attn.item()

         focus_scores