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..
(How) Do Language Models Track State?
Authors: Belinda Z. Li, Zifan Carl Guo, Jacob Andreas
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study state tracking in LMs trained or fine-tuned to compose permutations (i.e., to compute the order of a set of objects after a sequence of swaps). ... We show that LMs consistently learn one of two state tracking mechanisms for this task. ... Finally, 4 and 5 present experimental findings. Across a range of sizes, architectures, and pretraining schemes, we find that LMs consistently learn one of two state tracking mechanisms. |
| Researcher Affiliation | Academia | 1MIT EECS and CSAIL. Correspondence to: Belinda Z. Li <EMAIL>. |
| Pseudocode | Yes | 3.1. Sequential Algorithm ht,0 = at t // initialize actions (h0,0 = st) // by definition; see 2.2 for t = 1..T, l = 1..L do if l < t then ht,l = ht,l 1 = at // propagate actions if l = t then ht,l = ht 1,l 1ht,l 1 = st 1at = st // update states if l > t then ht,l = ht,l 1 = st // propagate states end for |
| Open Source Code | Yes | Code and data are available at https: //github.com/belindal/state-tracking. |
| Open Datasets | Yes | We generate 1 million unique length-100 sequences of permutations in both S3 and S5. ... Except where noted, we begin with Pythia-160M models pre-trained on the Pile dataset (Biderman et al., 2023). |
| Dataset Splits | Yes | We generate 1 million unique length-100 sequences of permutations in both S3 and S5. We split the data 90/10 for training/analysis, and fine-tune these models (using a crossentropy loss) to predict the state corresponding to each prefix of each action sequence: |
| Hardware Specification | No | For larger models (above 700M parameters), we train using bfloat16. No specific hardware models (e.g., GPU or CPU names) are provided. |
| Software Dependencies | No | The paper mentions 'Adam W optimizer' and 'Pythia-160M models pre-trained on the Pile dataset' and 'GPT-2', but does not provide specific version numbers for underlying software libraries like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | Regardless of initialization scheme, we fine-tune models for 20 epochs on Equation (3) using the Adam W optimizer with learning rate 5e-5 and batch size 128. |