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..
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
Authors: Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, Joseph E. Gonzalez
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of HIR extensively on 12 challenging Big Bench reasoning tasks and show that HIR outperforms the baseline algorithms and is comparable to or even surpasses supervised finetuning. |
| Researcher Affiliation | Academia | Tianjun Zhang * 1 Fangchen Liu * 1 Justin Wong 1 Pieter Abbeel 1 Joseph E. Gonzalez 1 1University of California, Berkeley. Correspondence to: Tianjun Zhang <EMAIL>, Fangchen Liu <fangchen EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Two-Stage Hindsight Instruction Relabeling (HIR) |
| Open Source Code | Yes | The implementation of HIR is available at https://github.com/tianjunz/HIR. |
| Open Datasets | Yes | We evaluate our algorithm extensively on 12 Big Bench (Srivastava et al., 2022) language model reasoning tasks. |
| Dataset Splits | No | To be specific, we divide the task data into 80% for training and 20% for testing. |
| Hardware Specification | No | We use the FLAN-T5 models (Chung et al., 2022) as the base model... |
| Software Dependencies | No | PPO For this baseline, we adopt the implementation of trlx from Carper AI. We directly use the Git Hub repository and load the FLAN-T5-large as the base model. |
| Experiment Setup | Yes | A. Training and Implementation Details A.1. Hyperparameters We provide all the hyperparameters we used in our experiments. This includes all the experiment settings we used for the baselines and our method. |