Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.36352

StructRL: Online Structured Reinforcement Learning for Long-Horizon Vision-Language-Action Tasks

Ziyi Yin·Sangmin Woo·Kang Zhou·Sungyeon Kim·Aosong Feng·Haibo Ding·Jun Huan
View original source ↗Read source document ↗Export metadata ↓

Abstract

Vision-language-action (VLA) models perform well on shorter-horizon manipulation tasks but still struggle with long-horizon tasks that require multiple dependent manipulations from a single command. Online reinforcement learning (RL) can improve these policies through environment interaction, yet many existing methods provide reward only after the complete task succeeds. However, such terminal supervision is sparse and does not distinguish early failures from rollouts that make substantial partial progress. We propose StructRL, an online RL framework that constructs structured intermediate supervision from verifiable subtask completions. StructRL decomposes each task into verifiable subtasks, grants intermediate rewards only after the prerequisite subtasks have been completed, and scales each reward according to completion pace. Across RoboCasa365 and LIBERO-Long with GR00T-N1.5 and pi 0.5, StructRL consistently outperforms evaluated online RL baselines. These results show that verifiable, structured intermediate rewards improve long-horizon VLA post-training. Code is available at https://github.com/amazon-science/StructRL.

Explore related subjects

cs.CVcs.LGcs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ziyi Yin, Sangmin Woo, Kang Zhou, Sungyeon Kim, Aosong Feng, Haibo Ding, Jun Huan. 2026-09-28. StructRL: Online Structured Reinforcement Learning for Long-Horizon Vision-Language-Action Tasks. https://arxiv.org/abs/2609.36352

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Event-based Scene Synthesis via Inter-Frame Residual Alignment

Event-based scene synthesis reconstructs target RGB frames from sparse image observations and asynchronous event streams, encompassing both video frame prediction and interpolation. Existing event-based synthesis methods commonly estimate optical flow to warp the observed frames toward the target time, but are vulnerable to inaccurate flow under large motion and occlusion and often rely on flow supervision or pretrained estimators. In this work, we propose EvFRA, an Event-based scene synthesis framework based on inter-Frame Residual Alignment. We identify a structural correspondence between event measurements and frame-to-frame scene changes, and exploit this correspondence for target frame synthesis. Our training pipeline consists of two stages: 1) an Event-to-Residual Alignment Variational Autoencoder (ER-VAE) aligns the event frame captured between the anchor and target frames with the corresponding inter-frame residual, and 2) a ControlNet-conditioned diffusion model is fine-tuned to denoise the residual latent using event data. Our method outperforms state-of-the-art methods by up to 2.61 dB and 1.85 dB in PSNR for frame prediction and interpolation, respectively, with consistent SSIM improvements. Code is available at https://github.com/jiyun-kong/EvFRA.

cs.CV↗
arXiv2026

From Concept Erasure to Style Purification: Contrastive Eigenbases for Artist Style Protection

Text-to-image diffusion models can reproduce specific artists visual styles at extremely low cost, raising copyright and deployment safety concerns about unauthorized style mimicry. Existing model-side protection methods generally follow ordinary concept erasure, emphasizing aggressive deletion or redirection of target styles. However, our causal intervention analysis shows that the central issue is not insufficient erasure strength, but a mismatch between artist styles and this paradigm: unlike ordinary object concepts, artist styles do not form compact, localized editable semantic units. Consequently, sparse editing and fixed retain lists struggle to suppress target styles while preserving generation utility. We therefore reformulate artist style protection as style purification, suppressing target style expression during inference while preserving the requested content and visual structure. We propose CAPE (Contrastive Artist Style Purification with Eigenbases), a training-free framework against artist style mimicry. CAPE constructs contrastive triplets around the target request and formulates style direction estimation as a generalized eigenvalue problem, capturing style-related directions that remain stable across content variations and are less affected by shared content. During inference, CAPE employs the Adaptive Suppression Controller to assign suppression strengths to different tokens based on Q, K, and V responses, and performs target style suppression on the K and V paths of self-attention. Experimental results show that CAPE effectively weakens target artist characteristics, including brushstrokes, textures, and local color processing, while better preserving major semantic entities, scene composition, and visual structures.

cs.CV↗
arXiv2026

Evaluating Generative Models via One-Dimensional Code Distributions

Most evaluations of generative models rely on feature-distribution metrics such as FID, which operate on continuous recognition features that are explicitly trained to be invariant to appearance variations, and thus discard cues critical for perceptual quality. We instead evaluate models in the space of discrete visual tokens, where modern 1D image tokenizers compactly encode both semantic and perceptual information and quality manifests as predictable token statistics. We introduce Codebook Histogram Distance (CHD), a training-free distribution metric in token space, and Code Mixture Model Score (CMMS), a no-reference quality metric learned from synthetic degradations of token sequences. To stress-test metrics under broad distribution shifts, we further propose VisForm, a benchmark of 210K images spanning 62 visual forms and 12 generative models with expert annotations. Across AGIQA, HPDv2/3, and VisForm, our token-based metrics achieve state-of-the-art correlation with human judgments. We will release all code and datasets to facilitate future research, with the code publicly available at https://github.com/zexiJia/1d-Distance.

cs.CV↗