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ARXIV:2603.14608 · REINFORCEMENT LEARNING · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.14608REINFORCEMENT LEARNINGSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
Delightful Policy Gradient improves policy gradient methods by addressing action weighting issues in reinforcement learning.
Opportunity summary
Pain Delightful Policy Gradient improves policy gradient methods by addressing action weighting issues in reinforcement learning.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Delightful Policy Gradient improves policy gradient methods by addressing action weighting issues in reinforcement learning. This creates two pathologies: within a single decision context (e.g.
Standard policy gradients weight each sampled action by advantage alone, regardless of how likely that action was under the current policy. This creates two pathologies: within a single decision context (e.g.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. For $K$-armed bandits, DG provably improves directional accuracy in a single context and, across multiple contexts, shifts the expected gradient strictly closer to the…
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Delightful Policy Gradient improves policy gradient methods by addressing action weighting issues in reinforcement learning.
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10.48550/arXiv.2603.14608Delightful Policy Gradient improves policy gradient methods by addressing action weighting issues in reinforcement learning.
Abstract
Standard policy gradients weight each sampled action by advantage alone, regardless of how likely that action was under the current policy. This creates two pathologies: within a single decision context (e.g. one image or prompt), a rare negative-advantage action can disproportionately distort the update direction; across many such contexts in a batch, the expected gradient over-allocates budget to contexts the policy already handles well. We introduce the \textit{Delightful Policy Gradient} (DG), which gates each term with a sigmoid of \emph{delight}, the product of advantage and action surprisal (negative log-probability). For $K$-armed bandits, DG provably improves directional accuracy in a single context and, across multiple contexts, shifts the expected gradient strictly closer to the supervised cross-entropy oracle. This second effect is not variance reduction: it persists even with infinite samples. Empirically, DG outperforms REINFORCE, PPO, and advantage-weighted baselines across MNIST, transformer sequence modeling, and continuous control, with larger gains on harder tasks.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
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PROBLEM
Delightful Policy Gradient improves policy gradient methods by addressing action weighting issues in reinforcement learning. This creates two pathologies: within a single decision context (e.g.
METHOD
Standard policy gradients weight each sampled action by advantage alone, regardless of how likely that action was under the current policy. This creates two pathologies: within a single decision context (e.g.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. For $K$-armed bandits, DG provably improves directional accuracy in a single context and, across multiple contexts, shifts the expected gradient strictly closer to the supervised cross-entropy oracle.
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Delightful Policy Gradient improves policy gradient methods by addressing action weighting issues in reinforcement learning. This creates two pathologies: within a single decision context (e.g.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Standard policy gradients weight each sampled action by advantage alone, regardless of how likely that action was under the current policy. This creates two pathologies: within a single decision context (e.g.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. For $K$-armed bandits, DG provably improves directional accuracy in a single context and, across multiple contexts, shifts the expected gradient strictly closer to the supervised cross-entropy oracle.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Delightful Policy Gradient improves policy gradient methods by addressing action weighting issues in reinforcement learning.
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Reinforcement Learning
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