NoiseGate learns per-latent timestep schedules as an information-gating policy in diffusion-based world action models, yielding consistent gains on RoboTwin manipulation tasks.
Rlinf: Flexible and efficient large-scale reinforcement learning via macro-to-micro flow transformation.arXiv preprint arXiv:2509.15965, 2025a
7 Pith papers cite this work. Polarity classification is still indexing.
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SpecRLBench is a new benchmark evaluating generalization of LTL-guided RL methods across navigation and manipulation domains with static/dynamic environments and varied robot dynamics.
PAIR-VLA adds invariance and sensitivity objectives over paired visual variants during PPO fine-tuning of VLA models, yielding 9-16% average gains on ManiSkill3 under distractors, textures, poses, viewpoints, and lighting shifts.
RAW-Dream lets VLAs learn new tasks in zero-shot imagination by using a world model pre-trained only on task-free behaviors and an unmodified VLM to supply rewards, with dual-noise verification to limit hallucinations.
DORA's multi-version streaming rollout enables 2-3x higher throughput in asynchronous RL for LLMs while preserving convergence by maintaining policy consistency, data integrity, and bounded staleness.
JigsawRL achieves up to 1.85x higher throughput in LLM RL pipelines via pipeline multiplexing, sub-stage graphs, and look-ahead scheduling compared to prior systems.
Sword improves world model simulators for VLA policies by disentangling visual style from dynamics and bootstrapping latents for better consistency, outperforming baselines on LIBERO in generalization and RL post-training success.
citing papers explorer
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NoiseGate: Learning Per-Latent Timestep Schedules as Information Gating in World Action Models
NoiseGate learns per-latent timestep schedules as an information-gating policy in diffusion-based world action models, yielding consistent gains on RoboTwin manipulation tasks.
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SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning
SpecRLBench is a new benchmark evaluating generalization of LTL-guided RL methods across navigation and manipulation domains with static/dynamic environments and varied robot dynamics.
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What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models
PAIR-VLA adds invariance and sensitivity objectives over paired visual variants during PPO fine-tuning of VLA models, yielding 9-16% average gains on ManiSkill3 under distractors, textures, poses, viewpoints, and lighting shifts.
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Reinforcing VLAs in Task-Agnostic World Models
RAW-Dream lets VLAs learn new tasks in zero-shot imagination by using a world model pre-trained only on task-free behaviors and an unmodified VLM to supply rewards, with dual-noise verification to limit hallucinations.
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DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
DORA's multi-version streaming rollout enables 2-3x higher throughput in asynchronous RL for LLMs while preserving convergence by maintaining policy consistency, data integrity, and bounded staleness.
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JigsawRL: Assembling RL Pipelines for Efficient LLM Post-Training
JigsawRL achieves up to 1.85x higher throughput in LLM RL pipelines via pipeline multiplexing, sub-stage graphs, and look-ahead scheduling compared to prior systems.
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Sword: Style-Robust World Models as Simulators via Dynamic Latent Bootstrapping for VLA Policy Post-Training
Sword improves world model simulators for VLA policies by disentangling visual style from dynamics and bootstrapping latents for better consistency, outperforming baselines on LIBERO in generalization and RL post-training success.