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..
Guiding Pretraining in Reinforcement Learning with Large Language Models
Authors: Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate ELLM in the Crafter game environment and the Housekeep robotic simulator, showing that ELLM-trained agents have better coverage of common-sense behaviors during pretraining and usually match or improve performance on a range of downstream tasks.4. Experiments Our experiments test the following hypotheses: |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA 2University of Washington, Seattle 3Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory 4Inria, Flowers Laboratory. |
| Pseudocode | Yes | Algorithm 1 ELLM Algorithm |
| Open Source Code | No | All code will be released soon, licensed under the MIT license (with Crafter, Housekeep licensed under their respective licenses). |
| Open Datasets | Yes | We evaluate ELLM in two complex environments: (1) Crafter, an open-ended environment in which exploration is required to discover long-term survival strategies (Hafner, 2021), and (2) Housekeep, an embodied robotics environment that requires common-sense to restrict the exploration of possible rearrangements of household objects (Kant et al., 2022). |
| Dataset Splits | No | No explicit mention of a validation dataset split or a methodology for creating one was found. Hyperparameter tuning was mentioned without specifying a validation set: 'In the Crafter environment, we swept over the following hyperparameters for the Oracle and Scratch (no-pretraining) conditions: learning rate, exploration decay schedule, and network update frequency.' (Section H). |
| Hardware Specification | Yes | We use NVIDIA TITAN Xps and NVIDIA Ge Force RTX 2080 Tis, with 2-3 seeds per GPU and running at roughtly 100ksteps/hour. |
| Software Dependencies | No | The paper mentions several models and algorithms used (e.g., DQN, Sentence BERT, GPT-2, CLIP ViT-B-32, Codex), but does not provide specific version numbers for underlying software libraries or dependencies (e.g., 'Python 3.x', 'PyTorch 1.x'). |
| Experiment Setup | Yes | Table 6: DQN Hyperparameters lists: "Frame Stack 4", "γ .99", "Seed Frames 5000", "n-step 3", "batch size 64", "lr 6.25e-5", "target update τ 1.0", "ϵ-min 0.01", "update frequency 4" (Section H). |