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..
Amortizing intractable inference in large language models
Authors: Edward J Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that this distributionmatching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. |
| Researcher Affiliation | Academia | Edward J. Hu*, Moksh Jain*, Eric Elmoznino Mila Quebec AI Institute, Universit e de Montr eal EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | Code for our experiments is available at https://github.com/GFNOrg/gfn-lm-tuning. |
| Open Datasets | Yes | We consider a dataset of prompts from Open Web Text (Gokaslan et al., 2019) with a 1.5B param GPT-2 XL (Radford et al., 2019) as the base model. We use the ROCStories corpus (Mostafazadeh et al., 2016) and SUBJ (Pang & Lee, 2004). |
| Dataset Splits | Yes | We obtained a dataset of 1000 prompts from Open Web Text (Gokaslan et al., 2019) that were each 1-3 sentences long, 50 of which were used for validation. |
| Hardware Specification | No | The research was enabled in part by computational resources provided by the Digital Research Alliance of Canada (https://alliancecan.ca), Mila (https://mila.quebec), and NVIDIA. |
| Software Dependencies | No | This was done with full fine-tuning using the trl library (von Werra et al., 2020). We use Lo RA (Hu et al., 2022) instead of full fine-tuning for hardware efficiency in all experiments. |
| Experiment Setup | Yes | We detail the hyperparameters used for training GFlow Nets in our experiments in Table C.2. We run GFlow Net fine-tuning for 1000 steps with a linear warmup over 200 steps, a fixed learning rate of 0.0005, and a batch size of 512 samples; see Table D.3 for all the hyperparameters used. We detail the hyperparameters used for training GFlow Nets in our experiments in Table E.2. |