The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning

The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning

PDF: https://arxiv.org/pdf/2604.01913v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.576Z

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Watch and verify: The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning

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Subject: The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning

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Claim map

Strong 7Mixed 1Weak 0

Evidencepartial
our theory attributes the loss of plasticity to two mechanisms: the rank collapse of the Neural Tangent Kernel (NTK) Gram matrix
Implicationpartial
Directly stated in abstract as a theoretical mechanism identified through formal characterization of non-stationarity factors
Verificationpartial
partial
Evidencepartial
our theory attributes the loss of plasticity to two mechanisms: ... and the $Θ(\frac{1}{k})$ decay of gradient magnitude
Implicationpartial
Directly stated in abstract as a second theoretical mechanism identified through formal analysis
Verificationpartial
partial
Evidencepartial
Sample Weight Decay -- a lightweight method to restore gradient magnitude, as a general remedy to plasticity loss for deep RL methods based on experience replay
Implicationpartial
Directly stated in abstract as the proposed method's purpose and supported by experimental evaluation across multiple algorithms
Verificationpartial
partial
Evidencepartial
The results demonstrate that \methodName effectively alleviates plasticity loss and consistently improves learning performance across various configurations of deep RL algorithms
Implicationpartial
Stated in abstract with experimental evaluation across multiple algorithms and environments, though specific performance metrics not provided
Verificationpartial
partial
Evidencepartial
achieving SOTA performance on challenging DMC Humanoid tasks
Implicationpartial
Directly stated in abstract as an experimental result, though specific comparison metrics not provided
Verificationpartial
partial
Evidencepartial
The first mechanism echoes prior empirical findings from the theoretical perspective and sheds light on the effects of existing methods, e.g., network reset, neuron recycle, and noise injection
Implicationpartial
Implied in abstract that these methods shed light on the effects of existing methods, suggesting they address the rank collapse mechanism
Verificationpartial
partial
Evidencepartial
which is orthogonal to existing methods
Implicationpartial
Directly stated that the method addresses gradient attenuation which is orthogonal to existing methods
Verificationpartial
partial
Evidencepartial
our understanding of how plasticity loss arises, dissipates, and can be dissolved remains limited to empirical findings, leaving the theoretical end underexplored
Implicationpartial
Explicitly stated in abstract as the research gap being addressed
Verificationpartial
partial

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The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning

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