Comparison
LLMSys-PaperList vs RLTF
Verdict
Pick LLMSys-PaperList if lLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems; pick RLTF if rLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.
Markdown twin · LLMSys-PaperList alternatives · RLTF alternatives
GraphCanon updated Oct 7, 2026
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Trust & integrity
| Signal | LLMSys-PaperList | RLTF |
|---|---|---|
| Maintenance | Steady (43d since push) As of Sep 6, 2026 · github_public_v1 | Dormant (731d since push) As of Oct 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 6, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- LLMSys-PaperList
- Curated list of academic papers related to Large Language Model systems
- RLTF
- Accepted by Transactions on Machine Learning Research (TMLR)
Stars
- LLMSys-PaperList
- 2.2k
- RLTF
- 134
Forks
- LLMSys-PaperList
- 120
- RLTF
- 7
Open issues
- LLMSys-PaperList
- 1
- RLTF
- 0
Language
- LLMSys-PaperList
- Python
- RLTF
- Python
Adopt for
- LLMSys-PaperList
- LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems.
- RLTF
- RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.
Persona
- LLMSys-PaperList
- -
- RLTF
- -
Runtime
- LLMSys-PaperList
- -
- RLTF
- -
License
- LLMSys-PaperList
- (unknown)
- RLTF
- BSD-3-Clause
Last pushed
- LLMSys-PaperList
- Jul 25, 2026
- RLTF
- Oct 5, 2024
Categories
- LLMSys-PaperList
- Inference & Serving, LLM Frameworks, Model Training
- RLTF
- Model Training
Trust and health
Maintenance
- LLMSys-PaperList
- Steady (60%)
- RLTF
- Dormant (18%)
Days since push
- LLMSys-PaperList
- 43d
- RLTF
- 731d
Open issues (now)
- LLMSys-PaperList
- 1
- RLTF
- 0
Stars delta
- LLMSys-PaperList
- +21 (30d)
- RLTF
- +1 (30d)
OSV dependency advisories
- LLMSys-PaperList
- No lockfile (source not queried)
- RLTF
- Published findings
Full report
- LLMSys-PaperList
- Trust report
- RLTF
- Trust report
Choose LLMSys-PaperList if…
- (repository does not specify hosting environment)
- Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers.
- Also covers Inference & Serving, LLM Frameworks.
- - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
When NOT to use LLMSys-PaperList
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models.
- - When your primary need is documentation or code examples rather than academic papers and project insights.
- - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
Choose RLTF if…
- Tags unique to RLTF: apps, bsd-license, code-rl, open-source-contributions.
- Use RLTF when you need advanced Reinforcement Learning models specifically tuned for text generation tasks.
- Leaner open-issue backlog (0).
When NOT to use RLTF
- Avoid RLTF if your project does not require reinforcement learning techniques, especially focused on text generation.
- Do not use this tool if your work is incompatible with components from CodeRL, APPS, or transformers.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AmberLJC/LLMSys-PaperList) · observed Sep 20, 2026
- GitHub forks (AmberLJC/LLMSys-PaperList) · observed Sep 20, 2026
- Last push (AmberLJC/LLMSys-PaperList) · observed Jul 25, 2026
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Zyq-scut/RLTF) · observed Oct 7, 2026
- GitHub forks (Zyq-scut/RLTF) · observed Oct 7, 2026
- Last push (Zyq-scut/RLTF) · observed Oct 5, 2024
- License file (BSD-3-Clause) · observed Oct 7, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMSys-PaperList 2.2k · RLTF 134 (synced Sep 20, 2026).
Common questions
- What is the difference between LLMSys-PaperList and RLTF?
- LLMSys-PaperList: Curated list of academic papers related to Large Language Model systems. RLTF: Accepted by Transactions on Machine Learning Research (TMLR). See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMSys-PaperList over RLTF?
- Choose LLMSys-PaperList over RLTF when (repository does not specify hosting environment); Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers; Also covers Inference & Serving, LLM Frameworks; - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
- When should I choose RLTF over LLMSys-PaperList?
- Choose RLTF over LLMSys-PaperList when Tags unique to RLTF: apps, bsd-license, code-rl, open-source-contributions; Use RLTF when you need advanced Reinforcement Learning models specifically tuned for text generation tasks; Leaner open-issue backlog (0).
- When should I avoid LLMSys-PaperList?
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - When your primary need is documentation or code examples rather than academic papers and project insights. - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
- When should I avoid RLTF?
- Avoid RLTF if your project does not require reinforcement learning techniques, especially focused on text generation. Do not use this tool if your work is incompatible with components from CodeRL, APPS, or transformers.
- Is LLMSys-PaperList or RLTF more popular on GitHub?
- LLMSys-PaperList has more GitHub stars (2,241 vs 134). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMSys-PaperList and RLTF open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMSys-PaperList or RLTF?
- GraphCanon lists graph-backed alternatives at LLMSys-PaperList alternatives and RLTF alternatives (LLMSys-PaperList markdown twin, RLTF markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, LLMSys-PaperList or RLTF?
- LLMSys-PaperList: Steady. RLTF: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for LLMSys-PaperList and RLTF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMSys-PaperList trust report; RLTF trust report.