Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR

Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 52
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID beyond-where-to-look-trajectory-guided-reinforcement-learning-for-multimodal-rlvr | Route /signal-canvas/beyond-where-to-look-trajectory-guided-reinforcement-learning-for-multimodal-rlvr

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Evidence Receipt

Route status: building

Claims: 12

References: 52

Proof: Verification pending

Freshness state: computing

Source paper: Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR

PDF: https://arxiv.org/pdf/2603.26126v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:54:52.872Z

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Watch and verify: Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR

/buildability/beyond-where-to-look-trajectory-guided-reinforcement-learning-for-multimodal-rlvr

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Subject: Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR

Verdict

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GitHub Code Pulse

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
we propose Trajectory-Guided Reinforcement Learning (TGRL), which guides the policy model to integrate visual evidence into fine-grained reasoning processes using expert reasoning trajectories from stronger models.
Implicationpartial
This is the core claim of the paper, stated in the abstract and supported by experimental results.
Verificationpartial
partial
Evidencepartial
Extensive experiments on multiple multimodal reasoning benchmarks demonstrate that TGRL consistently improves reasoning performance and effectively bridges the gap between visual perception and logical reasoning.
Implicationpartial
The abstract states this, and the experimental results tables show consistent improvements for TGRL-GRPO and TGRL-DAPO across various datasets.
Verificationpartial
partial
Evidencepartial
We further introduce token-level reweighting and trajectory filtering to ensure stable and effective policy optimization.
Implicationpartial
This is explicitly stated in the abstract as a key component of the proposed method.
Verificationpartial
partial
Evidencepartial
Removing trajectory reweighting or filtering consistently degrades performance, demonstrating the importance of properly utilizing expert trajectories.
Implicationpartial
The experimental results table directly compares TGRL with versions 'w/o Filter' and 'w/o Reweight', showing performance drops.
Verificationpartial
partial
Evidencepartial
Extensive experiments on multiple multimodal reasoning benchmarks demonstrate that TGRL consistently improves reasoning performance and effectively bridges the gap between visual perception and logical reasoning.
Implicationpartial
This is a stated outcome in the abstract, supported by the overall performance improvements shown in the experiments.
Verificationpartial
partial
Evidencepartial
TGRL incorporates expert trajectories into RLVR by modifying the rollout distribution, advantage normalization, and token-level importance weighting.
Implicationpartial
This is a detailed explanation of how TGRL works, provided in the 'Discussion' section.
Verificationpartial
partial
Evidencepartial
The resulting objectives preserve the underlying gradient structure of GRPO-style RLVR objectives while enabling trajectory-level alignment, achieving a principled balance between expert guidance and on-policy exploration.
Implicationpartial
This is a high-level summary of the method's benefit, stated in the 'Discussion' section.
Verificationpartial
partial
Evidencepartial
we propose Trajectory-Guided Reinforcement Learning (TGRL), which guides the policy model to integrate visual evidence into fine-grained reasoning processes using expert reasoning trajectories from stronger models.
Implicationpartial
This is the core claim of the paper, stated in the abstract and supported by experimental results showing performance improvements.
Verificationpartial
partial
Evidencepartial
We further introduce token-level reweighting and trajectory filtering to ensure stable and effective policy optimization.
Implicationpartial
The abstract explicitly mentions these components as part of the proposed method, and the experimental section discusses their importance.
Verificationpartial
partial
Evidencepartial
Extensive experiments on multiple multimodal reasoning benchmarks demonstrate that TGRL consistently improves reasoning performance and effectively bridges the gap between visual perception and logical reasoning.
Implicationpartial
The abstract states this, and the experimental results tables show consistent improvements for TGRL-GRPO and TGRL-DAPO over their non-trajectory-guided counterparts.
Verificationpartial
partial
Evidencepartial
Removing trajectory reweighting or filtering consistently degrades performance, demonstrating the importance of properly utilizing expert trajectories.
Implicationpartial
The experimental results directly compare TGRL with variants lacking filtering or reweighting, showing performance drops.
Verificationpartial
partial
Evidencepartial
Extensive experiments on multiple multimodal reasoning benchmarks demonstrate that TGRL consistently improves reasoning performance and effectively bridges the gap between visual perception and logical reasoning.
Implicationpartial
This is a key outcome highlighted in the abstract, supported by the overall performance improvements shown in the experiments.
Verificationpartial
partial

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Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR

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