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..
Active Fine-Tuning of Multi-Task Policies
Authors: Marco Bagatella, Jonas Hübotter, Georg Martius, Andreas Krause
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiment section is designed to evaluate active multi-task fine-tuning and provide an empirical answer to several questions. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Z urich, Z urich, Switzerland 2Max Planck Institute for Intelligent Systems, T ubingen, Germany 3University of T ubingen, T ubingen, Germany. Correspondence to: Marco Bagatella <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 AMF Input: initial policy π0, budget N, desired task distr. µc Output: fine-tuned policy πN Initialize dataset D0 = for n [0, . . . , N 1] do Compute cn as the solution to Eq. 2 Collect new demonstration τn for task cn if n + 1 % B = 0 then Dn+1 = Dn+1 B {cn B+1:n, τn B+1:n} Update πn+1 from πn+1 B with Dn+1 end if end for |
| Open Source Code | Yes | In order to ease reproducibility, we open-source our codebase on the project s repo.4 4github.com/marbaga/amf |
| Open Datasets | Yes | In Metaworld (Yu et al., 2020) we create a scene... In Franka Kitchen (Fu et al., 2020)... We consider the Robomimic benchmark... Octo is pretrained on a large-scale real-world robotic dataset (Collaboration, 2023)... |
| Dataset Splits | No | The paper describes how pre-training demonstrations are allocated across tasks and how evaluation is performed over task distributions, but does not specify fixed training/test/validation splits for the policy learning process itself. The data collection is interactive. |
| Hardware Specification | No | The paper mentions 'GPU acceleration' but does not specify any particular GPU models or other detailed hardware specifications for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'sentence transformer (all-Mini LM-L6-v2)' and 'Adam W optimizer' but does not provide specific version numbers for key software libraries or programming languages used for implementation. |
| Experiment Setup | Yes | The MLP policy has with 2 layers and 256 units per layer, with layer normalization (Ba, 2016). Policies are pre-trained for 200 epochs with batch size of 256, learning rate of 10 4 using the Adam W optimizer(Loshchilov & Hutter, 2019). Each fine-tuning round involves 3000 gradient steps, each with a batch size of 256. |