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 [1].
Safe-EF: Error Feedback for Non-smooth Constrained Optimization
Authors: Rustem Islamov, Yarden As, Ilyas Fatkhullin
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments in a reinforcement learning setup, simulating distributed humanoid robot training, validate the effectiveness of Safe-EF in ensuring safety and reducing communication complexity. Extensive experiments and ablation studies of Safe-EF, putting the method to the test on a challenging task of distributed humanoid robot training and providing important practical insights into the performance of non-smooth EF methods. |
| Researcher Affiliation | Academia | 1University of Basel, Switzerland 2ETH Zรผrich, Switzerland 3ETH AI Center, Switzerland. Correspondence to: Rustem Islamov <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Safe-EF with bidirectional compression |
| Open Source Code | Yes | For more specific details, please use our open-source implementation https://github.com/yardenas/safe-ef. |
| Open Datasets | No | The paper mentions using a |
| Dataset Splits | No | The paper mentions using a 'batch size Nfv = 1024' and 'a batch of 128 trajectories' which are training parameters. It also describes a synthetic data generation process. However, it does not specify explicit training, validation, or test dataset splits for any of the experiments (synthetic, Humanoid, Cartpole, or Neyman-Pearson classification). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models, or memory specifications. It generally refers to 'distributed humanoid robot training' but without hardware specifics. |
| Software Dependencies | No | The paper mentions several software components like 'PPO (Schulman et al., 2017)', 'Adam as optimizer (Kingma & Ba, 2014)', and 'Brax (Freeman et al., 2021)'. However, it does not provide specific version numbers for these software dependencies, which are required for a reproducible description. |
| Experiment Setup | Yes | Unless specified otherwise, in all our experiments, the default number of workers is n = 16, compression ratio is K/d = 0.1 with Top-K compression. We parameterize a neural network policy with d = 0.2M parameters and use a batch size Nfv = 1024 to evaluate fi and gi. We keep the default value ฮณ = 0.0003, with Adam as optimizer (Kingma & Ba, 2014). The only deviation from these parameters is the entropy regularization coefficient, which we set to 0.01 from 0.001. Table 1: The algorithms hyperparameters used in the training from Section 6.1. |