Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraints
Authors: Jay Bear, Adam Prugel-Bennett, Jonathon Hare
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark on the traveling salesperson problem to evaluate the capabilities of the modified system in an NP-hard problem where DT fails to learn. 5 Results on Easy-to-Hard Problems |
| Researcher Affiliation | Academia | Jay Bear Adam Prügel-Bennett Jonathon Hare The University of Southampton, Southampton, UK {jdh1g19,abp1,jsh2}@soton.ac.uk |
| Pseudocode | No | The paper uses diagrams and formal equations to describe the architecture and process, but does not include a distinct pseudocode or algorithm block. |
| Open Source Code | Yes | code for the experiments can be found at https://github.com/Jay-Bear/ rethinking-deep-thinking. |
| Open Datasets | Yes | We test on the three problem classes used by Bansal et al. [2] to evaluate DT-R, namely a prefix sum problem, a maze problem and a chess problem. from the Easy To Hard dataset [17] |
| Dataset Splits | Yes | shuffled and split into 80% training samples, 20% validation samples. |
| Hardware Specification | Yes | We have trained and evaluated the models on a range of different Nvidia GPU accelerators from RTX2080Tis to A100s, as well as on M3-series Apple Silicon. RTX8000, A100 |
| Software Dependencies | No | The paper mentions PyTorch [16] but does not specify its version. It also mentions Adam optimizer [12] but no version number is provided for it or any other software dependencies. |
| Experiment Setup | Yes | All models use the Adam optimizer [12] with a learning rate of 0.001, β1 = 0.9, β2 = 0.999, weight decay set to 0.0002 and only applied to unconstrained convolutional weights; incremental progress training with α = 0.5; exponential warmup with a warmup period of 3; a multi-step learning rate scheduler where milestones are calculated as a 8 : 4 : 2 : 1 ratio of the total number of epochs, with learning rates multiplied by 0.1 at each milestone. |