Academic

Does This Gradient Spark Joy?

arXiv:2603.20526v1 Announce Type: new Abstract: Policy gradient computes a backward pass for every sample, even though the backward pass is expensive and most samples carry little learning value. The Delightful Policy Gradient (DG) provides a forward-pass signal of learning value: \emph{delight}, the product of advantage and surprisal (negative log-probability). We introduce the \emph{Kondo gate}, which compares delight against a compute price and pays for a backward pass only when the sample is worth it, thereby tracing a quality--cost Pareto frontier. In bandits, zero-price gating preserves useful gradient signal while removing perpendicular noise, and delight is a more reliable screening signal than additive combinations of value and surprise. On MNIST and transformer token reversal, the Kondo gate skips most backward passes while retaining nearly all of DG's learning quality, with gains that grow as problems get harder and backward passes become more expensive. Because the gate to

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Ian Osband · · 1 min read · 7 views
#cs.LG #cs.AI #stat.ML

arXiv:2603.20526v1 Announce Type: new Abstract: Policy gradient computes a backward pass for every sample, even though the backward pass is expensive and most samples carry little learning value. The Delightful Policy Gradient (DG) provides a forward-pass signal of learning value: \emph{delight}, the product of advantage and surprisal (negative log-probability). We introduce the \emph{Kondo gate}, which compares delight against a compute price and pays for a backward pass only when the sample is worth it, thereby tracing a quality--cost Pareto frontier. In bandits, zero-price gating preserves useful gradient signal while removing perpendicular noise, and delight is a more reliable screening signal than additive combinations of value and surprise. On MNIST and transformer token reversal, the Kondo gate skips most backward passes while retaining nearly all of DG's learning quality, with gains that grow as problems get harder and backward passes become more expensive. Because the gate tolerates approximate delight, a cheap forward pass can screen samples before expensive backpropagation, suggesting a speculative-decoding-for-training paradigm.

Executive Summary

This article presents the Delightful Policy Gradient (DG) and the Kondo gate, a novel approach to reducing the computational cost of policy gradient methods in reinforcement learning. By introducing a forward-pass signal of learning value, called 'delight,' the algorithm determines whether a backward pass is worth the computational expense. The Kondo gate compares delight against a compute price, paying for a backward pass only when the sample is worth it. The authors demonstrate the effectiveness of the Kondo gate on various tasks, including bandits, MNIST, and transformer token reversal. The proposed method retains nearly all of DG's learning quality while reducing the number of backward passes, with gains that grow as problems get harder and backward passes become more expensive.

Key Points

  • ▸ Introduction of the Delightful Policy Gradient (DG) and the Kondo gate
  • ▸ Forward-pass signal of learning value, called 'delight'
  • ▸ Kondo gate compares delight against a compute price to determine whether a backward pass is worth the expense
  • ▸ Effective on various tasks, including bandits, MNIST, and transformer token reversal

Merits

Improved computational efficiency

The Kondo gate reduces the number of backward passes, resulting in significant computational savings

Demerits

Potential loss of information

Approximate delight may lead to a loss of information, particularly in scenarios where exact delight is crucial

Expert Commentary

This article presents a novel approach to reducing the computational cost of policy gradient methods in reinforcement learning. The introduction of the Delightful Policy Gradient (DG) and the Kondo gate is a significant contribution to the field. However, the potential loss of information due to approximate delight is a concern. Further research is needed to address this issue and ensure the accuracy and robustness of the proposed method. Nevertheless, the Kondo gate has the potential to revolutionize the field of reinforcement learning and enable the development of more efficient and scalable algorithms.

Recommendations

  • ✓ Further research is needed to address the potential loss of information due to approximate delight
  • ✓ Application of the Kondo gate to real-world problems, such as robotics, game playing, and autonomous vehicles

Sources

Original: arXiv - cs.LG

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