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pith:2025:BF4S6654AXYNQRTABTOED35P75
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Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model

Hanqing Wang, Jiahao Yuan, Jiamin Wang, Mingyu Liu, Shaoyang Wang, Yifan Han, Yiming Zhong, Yuexin Ma, Zemin Yang, Zhiqing Cui

Reinforcement learning via GRPO with a custom affordance reward function produces zero-shot generalization and emergent test-time reasoning in multimodal models for robot affordance grounding.

arxiv:2508.06206 v5 · 2025-08-08 · cs.RO · cs.CV

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Claims

C1strongest claim

Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Affordance-R1 achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities.

C2weakest assumption

The custom affordance function containing format, perception, and cognition rewards will steer the GRPO optimization toward generalizable cognitive reasoning rather than overfitting to training distributions or reward specifics, as implied by the claim of emergent capabilities from RL-only training.

C3one line summary

Affordance-R1 applies GRPO-based reinforcement learning to multimodal LLMs for affordance grounding, using format-perception-cognition rewards and the ReasonAff dataset to achieve zero-shot generalization and emergent reasoning.

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3 papers in Pith

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First computed 2026-05-21T01:04:15.766254Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

09792f7bbc05f0d846600cdc41efafff6834f3c836aa21e98cfff82244b8febf

Aliases

arxiv: 2508.06206 · arxiv_version: 2508.06206v5 · doi: 10.48550/arxiv.2508.06206 · pith_short_12: BF4S6654AXYN · pith_short_16: BF4S6654AXYNQRTA · pith_short_8: BF4S6654
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/BF4S6654AXYNQRTABTOED35P75 \
  | jq -c '.canonical_record' \
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Canonical record JSON
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