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..
Efficient Exploration for LLMs
Authors: Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, Benjamin Van Roy
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present evidence of substantial benefit from efficient exploration in gathering human feedback to improve large language models. In our experiments, an agent sequentially generates queries while fitting a reward model to the feedback received. |
| Researcher Affiliation | Collaboration | 1Google DeepMind 2Stanford University. |
| Pseudocode | Yes | Algorithm 1 learning interface |
| Open Source Code | No | The paper references third-party tools and libraries used (e.g., 'enn library'), but does not explicitly state that its own source code is released or provide a link to it. |
| Open Datasets | Yes | Each prompt is sampled uniformly from the Anthropic Helpfulness Base train dataset. |
| Dataset Splits | No | The paper mentions using 'Anthropic Helpfulness Base train dataset' and 'Anthropic Helpfulness Base eval dataset' but does not explicitly describe a distinct 'validation' split or provide specific train/validation/test split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'ADAM' for optimization and the 'enn library', but it does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | at the start of each epoch of interaction, each agents receives a batch of B = 32 prompts... The replay buffers are first-in-first-out (FIFO) buffer, with a maximum capacity of C = 3200 data points. In our experiments, we set N = 100. |