Comparison
Awesome-LLMs-ICLR-24 vs awesome-RLHF
Verdict
Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; pick awesome-RLHF if awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods.
Markdown twin · Awesome-LLMs-ICLR-24 alternatives · awesome-RLHF alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | Awesome-LLMs-ICLR-24 | awesome-RLHF |
|---|---|---|
| Maintenance | Dormant (856d since push) As of 1w · github_public_v1 | Steady (89d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Organization account As of 3d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- Awesome-LLMs-ICLR-24
- Compilation of LLM papers from ICLR 2024
- awesome-RLHF
- A curated list of reinforcement learning with human feedback resources (continually updated)
Stars
- Awesome-LLMs-ICLR-24
- 72
- awesome-RLHF
- 4.4k
Forks
- Awesome-LLMs-ICLR-24
- 5
- awesome-RLHF
- 258
Open issues
- Awesome-LLMs-ICLR-24
- 0
- awesome-RLHF
- 6
Language
- Awesome-LLMs-ICLR-24
- -
- awesome-RLHF
- -
Adopt for
- Awesome-LLMs-ICLR-24
- Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024.
- awesome-RLHF
- awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods.
Persona
- Awesome-LLMs-ICLR-24
- -
- awesome-RLHF
- -
Runtime
- Awesome-LLMs-ICLR-24
- -
- awesome-RLHF
- -
License
- Awesome-LLMs-ICLR-24
- MIT
- awesome-RLHF
- Apache-2.0
Last pushed
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
- awesome-RLHF
- May 20, 2026
Categories
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- awesome-RLHF
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- Awesome-LLMs-ICLR-24
- Dormant (18%)
- awesome-RLHF
- Steady (60%)
Days since push
- Awesome-LLMs-ICLR-24
- 856d
- awesome-RLHF
- 89d
Open issues (now)
- Awesome-LLMs-ICLR-24
- 0
- awesome-RLHF
- 6
Stars delta
- Awesome-LLMs-ICLR-24
- Unknown
- awesome-RLHF
- +9 (30d)
Open issues delta
- Awesome-LLMs-ICLR-24
- Unknown
- awesome-RLHF
- 0 (30d)
Owner type
- Awesome-LLMs-ICLR-24
- User
- awesome-RLHF
- Organization
Full report
- Awesome-LLMs-ICLR-24
- Trust report
- awesome-RLHF
- Trust report
Choose Awesome-LLMs-ICLR-24 if…
- License: Awesome-LLMs-ICLR-24 is MIT, awesome-RLHF is Apache-2.0.
- Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework.
- Also covers Developer Tools, Inference & Serving, LLM Frameworks.
- If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
When NOT to use Awesome-LLMs-ICLR-24
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024.
- For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
Choose awesome-RLHF if…
- License: awesome-RLHF is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT.
- Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models.
- When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems.
When NOT to use awesome-RLHF
- If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- GitHub forks (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- Last push (azminewasi/Awesome-LLMs-ICLR-24) · observed Apr 4, 2024
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (opendilab/awesome-RLHF) · observed Aug 17, 2026
- GitHub forks (opendilab/awesome-RLHF) · observed Aug 17, 2026
- Last push (opendilab/awesome-RLHF) · observed May 20, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMs-ICLR-24 72 · awesome-RLHF 4.4k (synced Aug 8, 2026).
Common questions
- What is the difference between Awesome-LLMs-ICLR-24 and awesome-RLHF?
- Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMs-ICLR-24 over awesome-RLHF?
- Choose Awesome-LLMs-ICLR-24 over awesome-RLHF when License: Awesome-LLMs-ICLR-24 is MIT, awesome-RLHF is Apache-2.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Inference & Serving, LLM Frameworks; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
- When should I choose awesome-RLHF over Awesome-LLMs-ICLR-24?
- Choose awesome-RLHF over Awesome-LLMs-ICLR-24 when License: awesome-RLHF is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT; Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems.
- When should I avoid Awesome-LLMs-ICLR-24?
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
- When should I avoid awesome-RLHF?
- If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process.
- Is Awesome-LLMs-ICLR-24 or awesome-RLHF more popular on GitHub?
- awesome-RLHF has more GitHub stars (4,422 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMs-ICLR-24 and awesome-RLHF open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, awesome-RLHF: Apache-2.0).
- Where can I find alternatives to Awesome-LLMs-ICLR-24 or awesome-RLHF?
- GraphCanon lists graph-backed alternatives at Awesome-LLMs-ICLR-24 alternatives and awesome-RLHF alternatives (Awesome-LLMs-ICLR-24 markdown twin, awesome-RLHF 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, Awesome-LLMs-ICLR-24 or awesome-RLHF?
- Awesome-LLMs-ICLR-24: Dormant. awesome-RLHF: Steady. 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 Awesome-LLMs-ICLR-24 and awesome-RLHF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMs-ICLR-24 trust report; awesome-RLHF trust report.