Subgoal Search For Complex Reasoning Tasks
Authors: Konrad Czechowski, Tomasz Odrzygóźdź, Marek Zbysiński, Michał Zawalski, Krzysztof Olejnik, Yuhuai Wu, Łukasz Kuciński, Piotr Miłoś
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically demonstrate the efficiency of MCTS-k Sub S and BF-k Sub S. In particular, we show that they vastly outperform their standard ( non-subgoal ) counterparts. As a testing ground, we consider three challenging domains: Sokoban, Rubik s Cube, and INT. All of them require non-trivial reasoning. |
| Researcher Affiliation | Collaboration | Konrad Czechowski University of Warsaw k.czechowski@mimuw.edu.pl Tomasz Odrzygó zd z University of Warsaw tomaszo@impan.pl Marek Zbysi nski University of Warsaw m.zbysinski@ students.mimuw.edu.pl Michał Zawalski University of Warsaw m.zawalski@uw.edu.pl Krzysztof Olejnik University of Warsaw k.olejnik3@ student.uw.edu.pl Yuhuai Wu University of Toronto, Vector Institute ywu@cs.toronto.edu Łukasz Kuci nski Polish Academy of Sciences lkucinski@impan.pl Piotr Miło s Polish Academy of Sciences, University of Oxford, deepsense.ai pmilos@impan.pl |
| Pseudocode | Yes | Algorithm 1 Best-First Subgoal Search (BF-k Sub S) [...] Algorithm 2 Low-level conditional policy [...] Algorithm 3 Subgoal generator |
| Open Source Code | Yes | We provide the code of our method and experiment settings at https://github.com/ subgoal-search/subgoal-search, and a dedicated website https://sites.google.com/ view/subgoal-search. |
| Open Datasets | Yes | 2The dataset for INT or Sokoban can be easily generated or are publicly available. For the Rubik s Cube, we use random data or simple heuristic (random data are often sufficient for robotic tasks and navigation.) ... INT [55] |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | The results were obtained on a server with an Intel Xeon E5-2630 v4 CPU and eight NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions software components like 'transformer architecture' and 'convolutional network' but does not specify their version numbers or the versions of any other software dependencies. |
| Experiment Setup | Yes | Table 1: BF-k Sub S hyperparameters. [...] In Table 1, we provide the values of the hyperparameters used in all experiments. |