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..
Learning to Reach Goals via Iterated Supervised Learning
Authors: Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, Sergey Levine
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We formally show that this iterated supervised learning procedure optimizes a bound on the RL objective, derive performance bounds of the learned policy, and empirically demonstrate improved goal-reaching performance and robustness over current RL algorithms in several benchmark tasks. |
| Researcher Affiliation | Academia | Dibya Ghosh UC Berkeley Abhishek Gupta UC Berkeley Ashwin Reddy UC Berkeley Justin Fu UC Berkeley Coline Devin UC Berkeley Benjamin Eysenbach Carnegie Mellon University Sergey Levine UC Berkeley |
| Pseudocode | Yes | Algorithm 1 Goal-Conditioned Supervised Learning (GCSL) |
| Open Source Code | Yes | We have additionally open-sourced our implementation at https://github.com/dibyaghosh/gcsl. |
| Open Datasets | Yes | Lunar Lander (Brockman et al., 2016) This environment requires a rocket to land in a specified region. |
| Dataset Splits | No | The paper describes data collection and training procedures but does not explicitly detail training/validation/test dataset splits with percentages or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not provide version numbers for this or any other software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | The GCSL loss is optimized using the Adam optimizer with learning rate α = 5 10 4, with a batch size of 256, taking one gradient step for every step in the environment. |