vla-r1-reasoning-embodied-agents

name	vla-r1-reasoning-embodied-agents
title	VLA-R1: Enhancing Reasoning in Vision-Language-Action Models
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2510.01623
keywords	["VLA","reasoning","embodied-AI","RLVR","robotics","chain-of-thought"]
description	Enhance embodied robot reasoning by integrating explicit chain-of-thought supervision with reinforcement learning from verifiable rewards (GRPO+RL). Use when improving robot decision-making for tasks requiring spatial reasoning and constraint satisfaction.

VLA-R1: Enhancing Reasoning in Vision-Language-Action Models

VLA-R1 addresses a critical gap in Vision-Language-Action models: they emit final actions directly without explicit reasoning over affordances, geometric relations, or constraints. By combining chain-of-thought supervision with RL from verifiable rewards, the model learns to reason about the task before acting.

Core Architecture

• Chain-of-Thought annotation: 13,000 examples with explicit reasoning steps (VLA-CoT-13K dataset)
• Supervised fine-tuning phase: Train reasoning head alongside action prediction
• Reinforcement learning phase: Optimize reasoning quality via GRPO with verifiable geometric rewards
• Multimodal grounding: Maintains visual understanding while developing reasoning capability

Implementation Steps

Setup reasoning-enhanced VLA framework:

# Initialize VLA-R1 training system
from vla_r1 import ReasoningVLATrainer, VLACoTDataset

# Load base VLA model with reasoning extension
vla_model = ReasoningVLATrainer(
    base_model="Qwen2.5-VL-7B",
    include_reasoning_head=True,
    reasoning_budget=256,  # tokens for CoT
    action_head_architecture="standard"
)

# Load VLA-CoT-13K dataset with reasoning annotations
dataset = VLACoTDataset(
    num_examples=13000,
    domains=["tabletop_manipulation", "grasping", "placement"],
    reasoning_style="explicit_affordance"
)

Execute SFT phase with reasoning supervision:

# Stage 1: Supervised fine-tuning with chain-of-thought
sft_trainer = vla_model.create_sft_trainer(
    learning_rate=1e-4,
    batch_size=8,
    num_epochs=2
)

# Train with reasoning and action targets
sft_losses = []
for batch in dataset:
    reasoning_output = vla_model.generate_reasoning(batch["image"])
    action_logits = vla_model.predict_action(
        image=batch[],
        reasoning=reasoning_output,
        instruction=batch[]
    )

    
    reasoning_loss = compute_language_loss(
        predictions=reasoning_output,
        targets=batch[]
    )

    action_loss = compute_action_loss(
        predictions=action_logits,
        targets=batch[]
    )

    total_loss = reasoning_loss + action_loss
    total_loss.backward()
    sft_losses.append(total_loss.item())