Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization explores A novel reinforcement learning paradigm for LLMs that bifurcates policy into normal and high-entropy modes to improve exploration without sacrificing accuracy.. Commercial viability score: 7/10 in LLM Reinforcement Learning.
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/buildability/policy-split-incentivizing-dual-mode-exploration-in-llm-reinforcement-with-dual-mode-entropy-regularization
Subject: Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization
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This equation captures one of the core mathematical components of the system. std {bj,s}G,|oj| j=1,s=1 bi,t = β1HHE i,t + β2DKL(πHE θ |πθ). Formally, given a policy πθ and system prompt s, the polic
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/buildability/policy-split-incentivizing-dual-mode-exploration-in-llm-reinforcement-with-dual-mode-entropy-regularization
Paper ref
policy-split-incentivizing-dual-mode-exploration-in-llm-reinforcement-with-dual-mode-entropy-regularization
arXiv id
2604.11510
Generated at
2026-04-15T16:44:43.510Z
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stale
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2026-04-15T16:44:43.510Z
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3
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50%
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9c867e2fb6f501b5fb0e4ab551fa4041a9af6470c1b028f73890e81ef2d5385d
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This equation captures one of the core mathematical components of the system. coefficient for advantange weight η > 0 and the clamping threshold κ ≥1, when H and DKL rep- resent entropy and KL diver
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This equation captures one of the core mathematical components of the system. bt = −α log πHE θ (ot|q, o<t) πHE θ (·|q) = πθ(·|s, q). −(1 −α) log πθ(ot|q, o<t).
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