Jump-Start Reinforcement Learning with Vision-Language-Action Regularization

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Vision-Language-Action Jump-Starting (VLAJS) is a novel method designed to enhance the sample efficiency of on-policy reinforcement learning (RL) for robotic manipulation. It addresses challenges like inefficient exploration and poor credit assignment in long-horizon tasks with sparse or imperfect rewards. VLAJS integrates sparse, low-frequency action suggestions from pretrained Vision-Language-Action (VLA) models, such as OpenVLA, into RL training. This guidance is incorporated via a directional action-consistency loss within a Proximal Policy Optimization (PPO) framework, biasing early exploration without enforcing strict imitation. The guidance is transient, applied sparsely, and annealed over time based on reward improvement, allowing the RL agent to adapt and eventually surpass the guiding policy. Evaluated on six ManiSkill manipulation tasks and validated on a real Franka Panda robot, VLAJS consistently reduced required environment interactions by over 50% in several tasks and demonstrated zero-shot sim-to-real transfer and robust execution under disturbances.

Key takeaway

For research scientists developing robotic manipulation policies, VLAJS offers a robust approach to overcome inefficient exploration and poor credit assignment in complex tasks. You should consider integrating sparse, transient VLA guidance with a directional action-consistency loss into your PPO-based RL frameworks. This method significantly improves sample efficiency and enables zero-shot sim-to-real transfer, allowing your policies to learn faster and perform reliably in real-world, dynamic environments.

Key insights

VLAJS jump-starts RL with sparse, transient VLA guidance, improving sample efficiency and real-world robot control.

Principles

• Transient guidance accelerates early learning.
• Directional loss prevents over-constraining the policy.
• Reward-based annealing deactivates guidance adaptively.

Method

VLAJS augments PPO with a directional action-consistency regularization, aligning RL agent actions with sparse, low-frequency VLA suggestions. Guidance is temporally discretized and adaptively annealed based on reward improvement, deactivating when learning accelerates.

In practice

• Use VLAJS for long-horizon robotic tasks.
• Apply directional loss for flexible VLA guidance.
• Implement reward-based deactivation for transient teacher use.

Topics

• Reinforcement Learning
• Vision-Language-Action Models
• Robotic Manipulation
• Proximal Policy Optimization
• Sample Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.