# From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction

arXiv: https://arxiv.org/abs/1804.10974 · HTML: https://mlsanity.com/paper/1804.10974/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Exploring Supervised and Unsupervised Rewards in Machine Translation](https://mlsanity.com/paper/2102.11403/) (2021)

## Abstract (arXiv)

In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction.