RISE proposes a self-evolving VLM framework with three designs to address challenges in question generation and solver adaptation, reporting consistent gains on seven benchmarks across two backbones.
Position: Will we run out of data? limits of llm scaling based on human-generated data
2 Pith papers cite this work. Polarity classification is still indexing.
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Withdrawal rights paired with centralized cost-based assignment prevent subsidy waste by collecting data only when the improvement threshold is sustainably reachable, turning infeasible cases into null outcomes.
citing papers explorer
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RISE: Reliable Improvement in Self-Evolving Vision-Language Models
RISE proposes a self-evolving VLM framework with three designs to address challenges in question generation and solver adaptation, reporting consistent gains on seven benchmarks across two backbones.
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Incentivizing User Data Contributions for LLM Improvement under Withdrawal Rights
Withdrawal rights paired with centralized cost-based assignment prevent subsidy waste by collecting data only when the improvement threshold is sustainably reachable, turning infeasible cases into null outcomes.