Stable-Layers: Fine-Tuning Image Layer Decomposition Models with VLM-Scored Reinforcement Learning

Hugging Face Daily Papers Papers
reinforcement-learning fine-tuning image-decomposition vision-language-model lora grpo layer-separation

Summary

Stable-Layers is a reinforcement learning framework that fine-tunes a pretrained image layer decomposition model using VLM feedback instead of paired supervision, employing Flow-GRPO with LoRA and a two-stage reward calibration pipeline to improve layer quality on the Crello dataset.

We present Stable-Layers, a reinforcement learning framework that eliminates the need for paired supervision by fine-tuning a pretrained layer decomposition model using only feedback from a vision-language model (VLM). Starting from Qwen-Image-Layered, we apply Flow-GRPO with LoRA adaptation, sampling multiple candidate decompositions per image, scoring them with a VLM, and optimising the policy from group-relative advantages. The key challenge lies in designing a reliable reward signal: VLMs scoring samples in isolation tend to compress their judgements into a narrow band, leaving GRPO with little within-group variance to learn from. We address this with a two-stage evaluation pipeline that pairs structured per-sample scoring across five edit-centric criteria with a grid-based calibration step in which the VLM re-scores all candidates side-by-side. Stable-Layers produces decompositions with stronger layer separation, fewer blank or artifact-heavy layers, and lower per-layer reconstruction error on the Crello dataset compared to the base model.
Original Article

Similar Articles

Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training

Hugging Face Daily Papers

ART (Art-based Reinforcement Training) enables parameter-efficient fine-tuning of frozen multimodal LLMs by optimizing raw visual input via gradient backpropagation, achieving performance comparable to LoRA while supporting pre-compiled computational graphs for high-throughput engines like vLLM.

When LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL

arXiv cs.LG

This paper frames LLM-generated reward shaping for sparse structured RL as a debugging problem, identifying failure modes like reward flooding and semantic misunderstanding. The authors propose diagnostic-driven iterative refinement, achieving dramatic success rate improvements (e.g., DoorKey-8×8 from 2.3% to 97.6%) compared to one-shot generation.

Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models

arXiv cs.LG

This paper presents VLM-Safe-RL, a framework that integrates frozen vision-language models into constrained MDP Lagrangian updates to provide anticipatory cost signals for safe reinforcement learning in high-speed visual control tasks. The method outperforms standard constraint-aware baselines on Safety-Gymnasium FormulaOne L2 and generalizes to held-out environments.