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ARXIV:2603.01694 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.01694REINFORCEMENT LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Leverage multi-view video data to shape rewards in reinforcement learning tasks.
Opportunity summary
Pain Leverage multi-view video data to shape rewards in reinforcement learning tasks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Leverage multi-view video data to shape rewards in reinforcement learning tasks. Recent studies have explored using image-text similarity produced by vision-language models (VLMs) to augment rewards of a task with visual feedback.
Reward design is of great importance for solving complex tasks with reinforcement learning. Recent studies have explored using image-text similarity produced by vision-language models (VLMs) to augment rewards of a task with visual feedback.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We confirm the efficacy of the proposed framework with extensive experiments on challenging humanoid locomotion tasks from HumanoidBench and manipulation tasks from MetaWorld, verifying…
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Leverage multi-view video data to shape rewards in reinforcement learning tasks.
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10.48550/arXiv.2603.01694Leverage multi-view video data to shape rewards in reinforcement learning tasks.
Abstract
Reward design is of great importance for solving complex tasks with reinforcement learning. Recent studies have explored using image-text similarity produced by vision-language models (VLMs) to augment rewards of a task with visual feedback. A common practice linearly adds VLM scores to task or success rewards without explicit shaping, potentially altering the optimal policy. Moreover, such approaches, often relying on single static images, struggle with tasks whose desired behavior involves complex, dynamic motions spanning multiple visually different states. Furthermore, single viewpoints can occlude critical aspects of an agent's behavior. To address these issues, this paper presents Multi-View Video Reward Shaping (MVR), a framework that models the relevance of states regarding the target task using videos captured from multiple viewpoints. MVR leverages video-text similarity from a frozen pre-trained VLM to learn a state relevance function that mitigates the bias towards specific static poses inherent in image-based methods. Additionally, we introduce a state-dependent reward shaping formulation that integrates task-specific rewards and VLM-based guidance, automatically reducing the influence of VLM guidance once the desired motion pattern is achieved. We confirm the efficacy of the proposed framework with extensive experiments on challenging humanoid locomotion tasks from HumanoidBench and manipulation tasks from MetaWorld, verifying the design choices through ablation studies.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Dimensions overall score 3.0
PROBLEM
Leverage multi-view video data to shape rewards in reinforcement learning tasks. Recent studies have explored using image-text similarity produced by vision-language models (VLMs) to augment rewards of a task with visual feedback.
METHOD
Reward design is of great importance for solving complex tasks with reinforcement learning. Recent studies have explored using image-text similarity produced by vision-language models (VLMs) to augment rewards of a task with visual feedback.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We confirm the efficacy of the proposed framework with extensive experiments on challenging humanoid locomotion tasks from HumanoidBench and manipulation tasks from MetaWorld, verifying the design choices...
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Leverage multi-view video data to shape rewards in reinforcement learning tasks. Recent studies have explored using image-text similarity produced by vision-language models (VLMs) to augment rewards of a task with visual feedback.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reward design is of great importance for solving complex tasks with reinforcement learning. Recent studies have explored using image-text similarity produced by vision-language models (VLMs) to augment rewards of a task with visual feedback.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We confirm the efficacy of the proposed framework with extensive experiments on challenging humanoid locomotion tasks from HumanoidBench and manipulation tasks from MetaWorld, verifying the design choices through ablation studies.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Leverage multi-view video data to shape rewards in reinforcement learning tasks.
Segment
Reinforcement Learning
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Commercial read
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partial
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