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simple_separated_format

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

simple_separated_format appears in the code Syntology harvested for 28 papers, as 3 distinct code bodies found in 28 places (a place is one code body under one paper). At least one of them ran in 26 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 simple_separated_format 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 1 of the 3 distinct code bodies named simple_separated_format; 2 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
1ran
2unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 of the 28 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

28 papers shown of 28, newest first; 28 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 2 papers added by Syntology; 1 papers have no page here and are shown by arXiv id only. 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
Diffusion Policy with Behavioral Advantage Correction for Offline Reinforcement Learning added by Syntology 2026-08 (from id) aviralkumar2907/CQL/d4rl/rlkit/core/tabulate.py 5cb0c55f58279f59 ran no licence file found · pointer only
PENG'S Q(λ) FOR CONSERVATIVE VALUE ESTIMATION IN OFFLINE REINFORCEMENT LEARNING added by Syntology 2026-05 (from id) hyeon1996/EPQ/rlkit/core/tabulate.py 5cb0c55f58279f59 ran no licence file found · pointer only
arXiv:2507.08387 2025-07 (from id) shlee94/Off2OnRL/rlkit/rlkit/core/tabulate.py 5cb0c55f58279f59 ran no licence file found · pointer only
PRISM: A Robust Framework for Skill-based Meta-Reinforcement Learning with Noisy Demonstrations 6 Feb 2025 katerakelly/oyster/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Continual Task Learning through Adaptive Policy Self-Composition 18 Nov 2024 charleshsc/CompoFormer/tabulate.py 5cb0c55f58279f59 ran Apache-2.0 (permissive)
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning 2 Nov 2024 charleshsc/HarmoDT/tabulate.py 5cb0c55f58279f59 ran Apache-2.0 (permissive)
Q-value Regularized Transformer for Offline Reinforcement Learning 27 May 2024 charleshsc/qt/tabulate.py 5cb0c55f58279f59 ran Apache-2.0 (permissive)
Adaptive $Q$-Aid for Conditional Supervised Learning in Offline Reinforcement Learning 3 Feb 2024 rail-berkeley/rlkit/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Policy-regularized Offline Multi-objective Reinforcement Learning 4 Jan 2024 qianlin04/prmorl/lib/diffusion/tabulate.py 5cb0c55f58279f59 ran no licence file found · pointer only
DiffAIL: Diffusion Adversarial Imitation Learning 11 Dec 2023 ml-group-sdu/diffail/rlkit/core/tabulate.py 5cb0c55f58279f59 ran no licence file found · pointer only
Accelerating Exploration with Unlabeled Prior Data 9 Nov 2023 avisingh599/cog/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills 30 Oct 2023 seongun-kim/vcrl/vcrl/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks 12 Jul 2023 IanChar/GPIDE/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations 28 Dec 2022 conglu1997/nppac/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Mutual Information Regularized Offline Reinforcement Learning 14 Oct 2022 sail-sg/MISA/viskit/tabulate.py fd06199912f135e9 unverified MIT (permissive)
Mildly Conservative Q-Learning for Offline Reinforcement Learning 9 Jun 2022 vitchyr/rlkit/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks 7 Oct 2021 UT-Austin-RPL/maple/maple/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning 19 Apr 2021 JieRen98/rlkit-pmoe/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning 16 Oct 2020 birlrobotics/rlkit_autotune/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Hybrid Discriminative-Generative Training via Contrastive Learning 17 Jul 2020 lhao499/HDGE/logger.py d6cc3c153cec4e1d unverified Apache-2.0 (permissive)
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers 24 Jun 2020 shreyasc-13/off_domain_rlkit/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Benchmarking Unsupervised Object Representations for Video Sequences 12 Jun 2020 ecker-lab/object-centric-representation-benchmark/ocrb/op3/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Conservative Q-Learning for Offline Reinforcement Learning 8 Jun 2020 ikostrikov/cql-results/d4rl/rlkit/core/tabulate.py 5cb0c55f58279f59 ran Apache-2.0 (permissive)
Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning 23 Dec 2019 richardrl/rlkit-relational/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Planning with Goal-Conditioned Policies 19 Nov 2019 snasiriany/leap/railrl/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Entity Abstraction in Visual Model-Based Reinforcement Learning 28 Oct 2019 jcoreyes/OP3/op3/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Contextual Imagined Goals for Self-Supervised Robotic Learning 23 Oct 2019 anair13/rlkit/rlkit/core/tabulate.py 5cb0c55f58279f59 ran MIT (permissive)
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables 19 Mar 2019 victorchan314/cs287_final_project/rlkit/core/tabulate.py 5cb0c55f58279f59 ran 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