BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning | ScienceToStartup | ScienceToStartup

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References (21)

[1]

Clip-Low Increases Entropy and Clip-High Decreases Entropy in Reinforcement Learning of Large Language Models

2025Jaesung R. Park, Junsu Kim et al.

[2]

Spurious Rewards: Rethinking Training Signals in RLVR

2025Rulin Shao, Shuyue Stella Li et al.

[3]

The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

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"Optimize LLM reinforcement learning with dynamic, probability-aware constraint intervals through BandPO to prevent entropy collapse."

LLM Reinforcement Learning•Score: 5•View PDF ↗

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BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning

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References (21)

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"Optimize LLM reinforcement learning with dynamic, probability-aware constraint intervals through BandPO to prevent entropy collapse."

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BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning

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Founder's Pitch

"Optimize LLM reinforcement learning with dynamic, probability-aware constraint intervals through BandPO to prevent entropy collapse."

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