Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Actor-Critic Algorithm for Sequence Prediction
Authors: Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, Yoshua Bengio
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling. |
| Researcher Affiliation | Academia | Dzmitry Bahdanau Philemon Brakel Kelvin Xu Anirudh Goyal Universit e de Montr eal Ryan Lowe Joelle Pineau Mc Gill University Aaron Courville Universit e de Montr eal Yoshua Bengio Universit e de Montr eal |
| Pseudocode | Yes | Algorithm 1 Actor-Critic Training for Sequence Prediction |
| Open Source Code | Yes | The source code is available at https://github.com/rizar/actor-critic-public |
| Open Datasets | Yes | We use text from the One Billion Word dataset for the spelling correction task (Chelba et al., 2013), which has pre-defined training and testing sets. [...] For our first translation experiment, we use data from the German-English machine translation track of the IWSLT 2014 evaluation campaign (Cettolo et al., 2014), as used in Ranzato et al. (2015), and closely follow the pre-processing described in that work. [...] In addition we considered a larger WMT14 English-French dataset Cho et al. (2014) with more than 12 million examples. |
| Dataset Splits | Yes | For the IWSLT 2014 data the sizes of validation and tests set were 6,969 and 6,750, respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | Yes | We thank the developers of Theano (Theano Development Team, 2016) and Blocks (van Merri enboer et al., 2015) for their great work. |
| Experiment Setup | Yes | We use the ADAM optimizer (Kingma & Ba, 2015) to train all the networks with the parameters recommended in the original paper, with the exception of the scale parameter α. The latter is first set to 10^-3 and then annealed to 10^-4 for log-likelihood training. For the pre-training stage of the actor-critic, we use α = 10^-3 and decrease it to 10^-4 for the joint actor-critic training. We used M = 1 sample for both actor-critic and REINFORCE. For exact hyperparameter settings we refer the reader to Appendix A. |