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  3. VAM: Verbalized Action Masking for Controllable Exploration
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VAM: Verbalized Action Masking for Controllable Exploration in RL Post-Training -- A Chess Case Study

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Stale evidence

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: stale

Source paper: VAM: Verbalized Action Masking for Controllable Exploration in RL Post-Training -- A Chess Case Study

PDF: https://arxiv.org/pdf/2602.16833v1

Source count: 0

Coverage: 33%

Last proof check: 2026-03-19T18:48:05.835Z

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VAM: Verbalized Action Masking for Controllable Exploration in RL Post-Training -- A Chess Case Study

Overall score: 5/10
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Canonical Paper Receipt

Last verification: 2026-03-19T18:48:05.835Z

Freshness: stale

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References: 0

Sources: 0

Coverage: 33%

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Prior Work
Vision-Language Models Unlock Task-Centric Latent Actions
Score 5.0stable
Higher Viability
VLM-Guided Experience Replay
Score 6.0up
Higher Viability
Self-Correcting VLA: Online Action Refinement via Sparse World Imagination
Score 6.0up
Higher Viability
SPAARS: Safer RL Policy Alignment through Abstract Exploration and Refined Exploitation of Action Space
Score 7.0up
Higher Viability
DreamPlan: Efficient Reinforcement Fine-Tuning of Vision-Language Planners via Video World Models
Score 8.0up
Higher Viability
AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models
Score 7.0up
Higher Viability
World2Act: Latent Action Post-Training via Skill-Compositional World Models
Score 8.0up
Higher Viability
Closed-Loop Verbal Reinforcement Learning for Task-Level Robotic Planning
Score 7.0up

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