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orthogonal_init

Syntologyentry name in harvested coderead from the graph 2026-09-24

orthogonal_init appears in the code Syntology harvested for 12 papers, as 12 distinct code bodies found in 12 places (a place is one code body under one paper). At least one of them ran in 4 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named orthogonal_init do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 4 of the 12 distinct code bodies named orthogonal_init; 8 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
0ran · fixture could not drive it
3ran
8unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 4 of the 12 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

12 papers shown of 12, newest first; 12 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 1 papers added by Syntology. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning added by Syntology 2026-05 (from id) ColinQiyangLi/qam/agents/model.py 97135b7e661acbd2 ran MIT (permissive)
Hyperspherical Normalization for Scalable Deep Reinforcement Learning 21 Feb 2025 SonyResearch/simba/scale_rl/networks/layers.py 5ac49b3249fc3ab0 ran · our draft was wrong Apache-2.0 (permissive)
Offline Behavior Distillation 30 Oct 2024 leaveslei/obd/data_lib/syndset.py 1ec0ea84d63a7360 unverified no licence file found · pointer only
Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration 23 Oct 2024 rail-berkeley/supe/supe/agents/model.py 7f86bee8890076da unverified MIT (permissive)
Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning 31 May 2024 Fang-Lin93/DAC/networks/initialization.py 87c04751d25ad6e2 ran GPL-3.0 (copyleft) · pointer only
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity 23 May 2024 johannesack/offlinerlstructurednonstationarity/RLMethods/rl_models.py 0e1815d7327a86a7 ran MIT (permissive)
TD-MPC2: Scalable, Robust World Models for Continuous Control 25 Oct 2023 nicklashansen/tdmpc/src/algorithm/tdmpc.py 20ae3914de637dd8 unverified MIT (permissive)
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation 17 Jul 2023 tliu1997/rnac/train_rnac.py aaec1511480533d6 unverified no licence file found · pointer only
History Compression via Language Models in Reinforcement Learning 24 May 2022 ml-jku/helm/model.py 60f8e59fbca2965d unverified licence not identified · pointer only
Goal Misgeneralization in Deep Reinforcement Learning 28 May 2021 UlisseMini/procgen-tools/procgen_tools/models.py 91b1e1870bc40fd3 unverified MIT (permissive)
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning 21 Jun 2020 alex-petrenko/sample-factory/sample_factory/model/utils.py ec56f732ad5319e0 unverified MIT (permissive)
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World 20 Mar 2017 xinjinghao/sparrow-v1/train_DDQN_vector.py 9b39a7fac162179b unverified MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections