p-doom/crowd-code-dataset-0.2 huggingface dataset:
[content] examples/jasmine.html (html)

      {
        "author":"Mihir Mahajan",
        "authorURL":"https://maharajamihir.github.io/",
        "affiliations": [{"name": "p(doom)", "url": "https://pdoom.org/"},
                         {"name": "TUM"}]
      },
[content] examples/jasmine.html (html)
We are at the cusp of an intelligence revolution. Neural networks are able to clone the behavior of peak human intellectual performance (cite Gemini/OAI IMO) given enough compute, data, and the right algorithms (R1). While an increasing amount of capital expenditure is allocated to compute clusters, and a well-working recipe of equipping models with the required priors and capacity to reason is publicly available, the path to human-level intelligence with the ability to automate large fractions of the economy will increasingly be shaped by paradigms that are able to find and efficiently use untouched data troves.

While product-feedback-loops (cite cursor) constitute an adaptive data trove, many domains like robotics are not mature enough to yield a product with wide enough adoption to create a feedback-loop of sufficient magnitude, begging the question of alternatives.

One paradigm proposed by the research community to overcome the data scarcity in those domains is that of world models. While world models can help frontier model development in numerous ways, an ambitious goal of the community is to train a world model to act as a simulation of the world (cite Genie 1, Genie 2), in order to train an agent in that simulation, via an adaptive curriculum (cite Michael Dennis) or otherwise.
[content] examples/jasmine.html (html)
 While an increasing amount of capital expenditure is allocated to compute clusters, and a well-working recipe of equipping models with the required priors and capacity to reason is publicly available, the path to human-level intelligence with the ability to automate large fractions of the economy will increasingly be shaped by paradigms that are able to find and efficiently use untouched data troves.
[content] examples/jasmine.html (html)

      {
        "author":"Franz Srambical",
        "authorURL":"https://srambical.fr/",
        "affiliations": [{"name": "p(doom)", "url": "https://pdoom.org/"},
                         {"name": "TUM"}]
      }
[content] examples/bibliography.bib (bibtex)

@misc{pfau2023last,
  title = {Last I checked, it was still not possible for a neural network alone
           (i.e. no MCTS) to beat the world's best Go players...},
  author = {Pfau, David},
  year = {2023},
  url = {https://twitter.com/pfau/status/1732785418565796167},
  note = {Accessed: 2023-12-07},
}
[content] examples/bibliography.bib (bibtex)
@misc{deepmind2025imo,
  title = {Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad},
  author = {Luong, Thang and Lockhart, Edward},
  year = {2025},
  url = {https://deepmind.google/discover/blog/advanced-version-of-gemini-with-deep-think-officially-achieves-gold-medal-standard-at-the-international-mathematical-olympiad/},
  note = {DeepMind Blog, July 21, 2025},
}
[content] examples/bibliography.bib (bibtex)
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
      title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, 
      author={DeepSeek-AI},
      year={2025},
      eprint={2501.12948},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.12948}, 
}
[content] examples/bibliography.bib (bibtex)
@inproceedings{
    bruce2024genie,
    title={Genie: Generative Interactive Environments},
    author={Jake Bruce and Michael D Dennis and Ashley Edwards and Jack Parker-Holder and Yuge Shi and Edward Hughes and Matthew Lai and Aditi Mavalankar and Richie Steigerwald and Chris Apps and Yusuf Aytar and Sarah Maria Elisabeth Bechtle and Feryal Behbahani and Stephanie C.Y. Chan and Nicolas Heess and Lucy Gonzalez and Simon Osindero and Sherjil Ozair and Scott Reed and Jingwei Zhang and Konrad Zolna and Jeff Clune and Nando de Freitas and Satinder Singh and Tim Rockt{\"a}schel},
    booktitle={Forty-first International Conference on Machine Learning},
    year={2024},
    url={https://openreview.net/forum?id=bJbSbJskOS}
}
[content] examples/bibliography.bib (bibtex)
    Jack Parker-Holder, Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi, Stephen Spencer, Jessica Yung, Michael Dennis, Sultan Kenjeyev, Shangbang Long, Vlad Mnih, Harris Chan, Maxime Gazeau, Bonnie Li, Fabio Pardo, Luyu Wang, Lei Zhang, Frederic Besse, Tim Harley, Anna Mitenkova, Jane Wang, Jeff Clune, Demis Hassabis, Raia Hadsell, Adrian Bolton, Satinder Singh, Tim Rocktäschel
[content] examples/bibliography.bib (bibtex)
  author = {Jack Parker-Holder and Philip Ball and Jake Bruce and Vibhavari Dasagi and Kristian Holsheimer and Christos Kaplanis and Alexandre Moufarek and Guy Scully and Jeremy Shar and Jimmy Shi and Stephen Spencer and Jessica Yung and Michael Dennis and Sultan Kenjeyev and Shangbang Long and Vlad Mnih and Harris Chan and Maxime Gazeau and Bonnie Li and Fabio Pardo and Luyu Wang and Lei Zhang and Frederic Besse and Tim Harley and Anna Mitenkova and Jane Wang and Jeff Clune and Demis Hassabis and Raia Hadsell and Adrian Bolton and Satinder Singh and Tim Rocktäschel},
[content] examples/bibliography.bib (bibtex)

@misc{deepmind2025genie2,
  title = {Genie 2: A large-scale foundation world model},
  author = {Jack Parker-Holder and Philip Ball and Jake Bruce and Vibhavari Dasagi and Kristian Holsheimer and Christos Kaplanis and Alexandre Moufarek and Guy Scully and Jeremy Shar and Jimmy Shi and Stephen Spencer and Jessica Yung and Michael Dennis and Sultan Kenjeyev and Shangbang Long and Vlad Mnih and Harris Chan and Maxime Gazeau and Bonnie Li and Fabio Pardo and Luyu Wang and Lei Zhang and Frederic Besse and Tim Harley and Anna Mitenkova and Jane Wang and Jeff Clune and Demis Hassabis and Raia Hadsell and Adrian Bolton and Satinder Singh and Tim Rocktäschel},
  year = {2025},
  url = {https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/},
  note = {DeepMind Blog, December 4, 2024},
}
[content] examples/bibliography.bib (bibtex)
@article{genie3,
  title         = {Genie 3: A New Frontier for World Models},
  author        = {Philip J. Ball and Jakob Bauer and Frank Belletti and Bethanie Brownfield and Ariel Ephrat and Shlomi Fruchter and Agrim Gupta and Kristian Holsheimer and Aleksander Holynski and Jiri Hron and Christos Kaplanis and Marjorie Limont and Matt McGill and Yanko Oliveira and Jack Parker-Holder and Frank Perbet and Guy Scully and Jeremy Shar and Stephen Spencer and Omer Tov and Ruben Villegas and Emma Wang and Jessica Yung and Cip Baetu and Jordi Berbel and David Bridson and Jake Bruce and Gavin Buttimore and Sarah Chakera and Bilva Chandra and Paul Collins and Alex Cullum and Bogdan Damoc and Vibha Dasagi and Maxime Gazeau and Charles Gbadamosi and Woohyun Han and Ed Hirst and Ashyana Kachra and Lucie Kerley and Kristian Kjems and Eva Knoepfel and Vika Koriakin and Jessica Lo and Cong Lu and Zeb Mehring and Alex Moufarek and Henna Nandwani and Valeria Oliveira and Fabio Pardo and Jane Park and Andrew Pierson and Ben Poole and Helen Ran and Tim Salimans and Manuel Sanchez and Igor Saprykin and Amy Shen and Sailesh Sidhwani and Duncan Smith and Joe Stanton and Hamish Tomlinson and Dimple Vijaykumar and Luyu Wang and Piers Wingfield and Nat Wong and Keyang Xu and Christopher Yew and Nick Young and Vadim Zubov and Douglas Eck and Dumitru Erhan and Koray Kavukcuoglu and Demis Hassabis and Zoubin Gharamani and Raia Hadsell and A{\"a}ron van den Oord and Inbar Mosseri and Adrian Bolton and Satinder Singh and Tim Rockt{\"a}schel},
  year          = {2025},
  url           = {}
}
[content] examples/bibliography.bib (bibtex)
@article{parkerholder2024genie2,
  title         = {Genie 2: A Large-Scale Foundation World Model},
  author        = {Jack Parker-Holder and Philip Ball and Jake Bruce and Vibhavari Dasagi and Kristian Holsheimer and Christos Kaplanis and Alexandre Moufarek and Guy Scully and Jeremy Shar and Jimmy Shi and Stephen Spencer and Jessica Yung and Michael Dennis and Sultan Kenjeyev and Shangbang Long and Vlad Mnih and Harris Chan and Maxime Gazeau and Bonnie Li and Fabio Pardo and Luyu Wang and Lei Zhang and Frederic Besse and Tim Harley and Anna Mitenkova and Jane Wang and Jeff Clune and Demis Hassabis and Raia Hadsell and Adrian Bolton and Satinder Singh and Tim Rockt{\"a}schel},
  year          = {2024},
  url           = {https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/}
}
[content] examples/bibliography.bib (bibtex)
@InProceedings{pmlr-v162-parker-holder22a,
  title = 	 {Evolving Curricula with Regret-Based Environment Design},
  author =       {Parker-Holder, Jack and Jiang, Minqi and Dennis, Michael and Samvelyan, Mikayel and Foerster, Jakob and Grefenstette, Edward and Rockt{\"a}schel, Tim},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {17473--17498},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/parker-holder22a/parker-holder22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/parker-holder22a.html},
  abstract = 	 {Training generally-capable agents with reinforcement learning (RL) remains a significant challenge. A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent’s capabilities. These methods benefit from theoretical robustness guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces in practice. By contrast, evolutionary approaches incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. This work proposes harnessing the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent’s capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive version of this paper is available at https://accelagent.github.io.}
}
[content] examples/jasmine.html (html)
@InProceedings{pmlr-v162-parker-holder22a,
  title = 	 {Evolving Curricula with Regret-Based Environment Design},
  author =       {Parker-Holder, Jack and Jiang, Minqi and Dennis, Michael and Samvelyan, Mikayel and Foerster, Jakob and Grefenstette, Edward and Rockt{\"a}schel, Tim},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {17473--17498},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/parker-holder22a/parker-holder22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/parker-holder22a.html},
  abstract = 	 {Training generally-capable agents with reinforcement learning (RL) remains a significant challenge. A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent’s capabilities. These methods benefit from theoretical robustness guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces in practice. By contrast, evolutionary approaches incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. This work proposes harnessing the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent’s capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive version of this paper is available at https://accelagent.github.io.}
}
[content] examples/jasmine.html (html)
While numerous previous works have investigated large-scale world modeling and its application to robotics (cite cosmos), world modeling for agent training necessitates a vastly different treatment. Such regime requires the accumulating precision error of the world model to be orders of magnitude smaller than when solely used for short-term look-ahead. The feasibility of such a world model in its truest sense is entirely understudied, and this project is a first attempt at studying the setting using rigorous evaluations. Specifically, we want to develop Empirical Environment Complexity Scaling Laws, where we train world models to full convergence in environments of increasing complexity (Atari, RetroGym, Craftax, Minecraft) and under the synthetic infinite-data regime. Subsequently, we evaluate those models two-fold: i) via a taxonomy of granular benchmarks probing specific world modeling capabilities (reconstruction quality, environment dynamics at the body/tail of the data distribution, long-horizon consistency) (cite BSuite), and ii) by training reinforcement learning (RL) agents in both the world model and the corresponding ground-truth environment, and measuring the performance difference between the two agents.

The final goal of this project is to construct empirical estimates of compute and data requirements to model environments of increasing complexity sufficiently well (as determined by our evaluation procedure). The conclusion to draw from such empirical estimate is entirely open. If our empirical estimates show resource requirement trends that are feasible under the assumption of the continuation of Moore’s Law (cite) and increased capital expenditure, that would manifest world modeling as a paradigm with high likelihood of success in overcoming the data-scarcity in domains as general as (humanoid) robotics. Otherwise, the world modeling research community must realign its direction with downstream goals that are feasible.
[content] examples/bibliography.bib (bibtex)
@article{agarwal2025cosmos,
  title = {Cosmos world foundation model platform for physical ai},
  author = {Agarwal, Niket and others},
  journal = {arXiv preprint arXiv:2501.03575},
  year = {2025},
}
[content] examples/bibliography.bib (bibtex)
@article{bellemare2013arcade,
  title = {The arcade learning environment: An evaluation platform for general agents},
  author = {Bellemare, Marc G and others},
  journal = {Journal of artificial intelligence research},
  volume = {47},
  pages = {253--279},
  year = {2013}
}
[content] examples/bibliography.bib (bibtex)
@article{nichol2018retro,
  title={Gotta Learn Fast: A New Benchmark for Generalization in RL},
  author={Nichol, Alex and Pfau, Vicki and Hesse, Christopher and Klimov, Oleg and Schulman, John},
  journal={arXiv preprint arXiv:1804.03720},
  year={2018}
}
[content] examples/bibliography.bib (bibtex)
@inproceedings{matthews2024craftax,
    author={Michael Matthews and Michael Beukman and Benjamin Ellis and Mikayel Samvelyan and Matthew Jackson and Samuel Coward and Jakob Foerster},
    title = {Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning},
    booktitle = {International Conference on Machine Learning ({ICML})},
    year = {2024}
}
[content] examples/bibliography.bib (bibtex)
@inproceedings{NEURIPS2022_9c7008af,
 author = {Baker, Bowen and Akkaya, Ilge and Zhokov, Peter and Huizinga, Joost and Tang, Jie and Ecoffet, Adrien and Houghton, Brandon and Sampedro, Raul  and Clune, Jeff},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {24639--24654},
 publisher = {Curran Associates, Inc.},
 title = {Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/9c7008aff45b5d8f0973b23e1a22ada0-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}
[content] examples/bibliography.bib (bibtex)
@inproceedings{osband2020bsuite,
    title={Behaviour Suite for Reinforcement Learning},
    author={Osband, Ian and
            Doron, Yotam and
            Hessel, Matteo and
            Aslanides, John and
            Sezener, Eren and
            Saraiva, Andre and
            McKinney, Katrina and
            Lattimore, Tor and
            {Sz}epesv{\'a}ri, Csaba and
            Singh, Satinder and
            Van Roy, Benjamin and
            Sutton, Richard and
            Silver, David and
            van Hasselt, Hado},
    booktitle={International Conference on Learning Representations},
    year={2020},
    url={https://openreview.net/forum?id=rygf-kSYwH}
}
[content] examples/bibliography.bib (bibtex)
@software{flax2020github,
  author = {Jonathan Heek and Anselm Levskaya and Avital Oliver and Marvin Ritter and Bertrand Rondepierre and Andreas Steiner and Marc van {Z}ee},
  title = {{F}lax: A neural network library and ecosystem for {JAX}},
  url = {http://github.com/google/flax},
  version = {0.11.0},
  year = {2024},
}
[content] examples/jasmine.html (html)
If our empirical estimates show resource requirement trends that are feasible under the assumption of the continuation of Moore’s Law (cite) and increased capital expenditure, that would manifest world modeling as a paradigm with high likelihood of success in overcoming the data-scarcity in domains as general as (humanoid) robotics. Otherwise, the world modeling research community must realign its direction with downstream goals that are feasible.
[content] examples/bibliography.bib (bibtex)
@article{nguyen2025crowd-sourcing,
  author = {Nguyen, Alfred and Mahajan, Mihir and Srambical, Franz},
  title = {Crowd-Sourcing A Dataset To Make Agents Code Like Humans},
  journal = {p(doom) blog},
  year = {2025},
  note = {https://pdoom.org/blog.html}
}
[content] examples/blog.html (html)

          <div class="post-preview">
              <div class="metadata">
                  <div class="publishedDate">June 22, 2025</div>
              </div>
              <a href="crowd_code.html">
                  <div class="thumbnail"><img src="crowd_code_preview.gif" style="border-radius: 8px;"></div>
                  <div class="description">
                      <h2 class="title">Crowd-Sourcing A Dataset To Make Agents Code Like Humans</h2>
                      <p class="authors">Alfred&nbsp;Nguyen, Mihir&nbsp;Mahajan, Franz&nbsp;Srambical</p>
                      <p class="abstract">We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.</p>
                  </div>
              </a>
          </div>
[content] examples/jasmine.html (html)

    <figure style="grid-column: page; margin: 1rem 0; display: flex; justify-content: center"><img src="crowd_code_preview.gif"
      style="width:100%; border-radius: 8px;" /></figure>
    <figcaption style="grid-column: page; text-align: center; margin-bottom: 2rem; font-size: 0.8em; color: rgba(0, 0, 0, 0.5);">Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.</figcaption>
[content] examples/jasmine.html (html)
<figure style="grid-column: page; margin: 1rem 0; display: flex; justify-content: center"><img src="jasmine_preview.gif"
  style="width:100%; border-radius: 8px;" /></figure>
<figcaption style="grid-column: page; text-align: center; margin-bottom: 2rem; font-size: 0.8em; color: rgba(0, 0, 0, 0.5);">Figure 1: Jasmine in action.</figcaption>