Comparison
awesome-RLHF vs Instruction-Tuning-Papers
Verdict
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; pick Instruction-Tuning-Papers if instruction-Tuning-Papers is a curated reading list focused on the instruction-tuning domain for language models.
Markdown twin · awesome-RLHF alternatives · Instruction-Tuning-Papers alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | awesome-RLHF | Instruction-Tuning-Papers |
|---|---|---|
| Maintenance | Steady (89d since push) As of 3d · github_public_v1 | Dormant (1113d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3d · github_public_v1 | Not a fork · Personal account As of 2w · 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-RLHF
- A curated list of reinforcement learning with human feedback resources (continually updated)
- Instruction-Tuning-Papers
- Reading list of Instruction-tuning papers.
Stars
- awesome-RLHF
- 4.4k
- Instruction-Tuning-Papers
- 768
Forks
- awesome-RLHF
- 258
- Instruction-Tuning-Papers
- 23
Open issues
- awesome-RLHF
- 6
- Instruction-Tuning-Papers
- 0
Language
- awesome-RLHF
- -
- Instruction-Tuning-Papers
- -
Adopt for
- 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.
- Instruction-Tuning-Papers
- Instruction-Tuning-Papers is a curated reading list focused on the instruction-tuning domain for language models.
Persona
- awesome-RLHF
- -
- Instruction-Tuning-Papers
- -
Runtime
- awesome-RLHF
- -
- Instruction-Tuning-Papers
- -
License
- awesome-RLHF
- Apache-2.0
- Instruction-Tuning-Papers
- -
Last pushed
- awesome-RLHF
- May 20, 2026
- Instruction-Tuning-Papers
- Jul 20, 2023
Categories
- awesome-RLHF
- Evaluation & Observability, Model Training
- Instruction-Tuning-Papers
- Model Training
Trust and health
Maintenance
- awesome-RLHF
- Steady (60%)
- Instruction-Tuning-Papers
- Dormant (18%)
Days since push
- awesome-RLHF
- 89d
- Instruction-Tuning-Papers
- 1113d
Open issues (now)
- awesome-RLHF
- 6
- Instruction-Tuning-Papers
- 0
Stars delta
- awesome-RLHF
- +9 (30d)
- Instruction-Tuning-Papers
- Unknown
Open issues delta
- awesome-RLHF
- 0 (30d)
- Instruction-Tuning-Papers
- Unknown
Owner type
- awesome-RLHF
- Organization
- Instruction-Tuning-Papers
- User
Full report
- awesome-RLHF
- Trust report
- Instruction-Tuning-Papers
- Trust report
Choose awesome-RLHF if…
- Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, reinforcement-learning.
- Also covers Evaluation & Observability.
- 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.
Choose Instruction-Tuning-Papers if…
- Tags unique to Instruction-Tuning-Papers: cross-task-generalization, instruction-tuning, multi-task learning, natural-language-processing.
- When you're looking to enhance your understanding of how natural language instructions can empower language models in diverse tasks.
- Leaner open-issue backlog (0).
When NOT to use Instruction-Tuning-Papers
- Avoid this resource if you are looking for tools or frameworks to implement instruction tuning rather than theoretical understanding.
- Not suitable for users in need of a broader overview beyond specific academic papers on language model training methodologies.
- If your interest lies more in general NLP resources or comprehensive toolkits, Instruction-Tuning-Papers may not cover all aspects.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (SinclairCoder/Instruction-Tuning-Papers) · observed Aug 6, 2026
- GitHub forks (SinclairCoder/Instruction-Tuning-Papers) · observed Aug 6, 2026
- Last push (SinclairCoder/Instruction-Tuning-Papers) · observed Jul 20, 2023
- License file (unknown) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-RLHF 4.4k · Instruction-Tuning-Papers 768 (synced Aug 17, 2026).
Common questions
- What is the difference between awesome-RLHF and Instruction-Tuning-Papers?
- awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). Instruction-Tuning-Papers: Reading list of Instruction-tuning papers.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-RLHF over Instruction-Tuning-Papers?
- Choose awesome-RLHF over Instruction-Tuning-Papers when Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, reinforcement-learning; Also covers Evaluation & Observability; 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 choose Instruction-Tuning-Papers over awesome-RLHF?
- Choose Instruction-Tuning-Papers over awesome-RLHF when Tags unique to Instruction-Tuning-Papers: cross-task-generalization, instruction-tuning, multi-task learning, natural-language-processing; When you're looking to enhance your understanding of how natural language instructions can empower language models in diverse tasks; Leaner open-issue backlog (0).
- 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.
- When should I avoid Instruction-Tuning-Papers?
- Avoid this resource if you are looking for tools or frameworks to implement instruction tuning rather than theoretical understanding. Not suitable for users in need of a broader overview beyond specific academic papers on language model training methodologies. If your interest lies more in general NLP resources or comprehensive toolkits, Instruction-Tuning-Papers may not cover all aspects.
- Is awesome-RLHF or Instruction-Tuning-Papers more popular on GitHub?
- awesome-RLHF has more GitHub stars (4,422 vs 768). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-RLHF and Instruction-Tuning-Papers open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-RLHF or Instruction-Tuning-Papers?
- GraphCanon lists graph-backed alternatives at awesome-RLHF alternatives and Instruction-Tuning-Papers alternatives (awesome-RLHF markdown twin, Instruction-Tuning-Papers 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-RLHF or Instruction-Tuning-Papers?
- awesome-RLHF: Steady. Instruction-Tuning-Papers: 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 awesome-RLHF and Instruction-Tuning-Papers?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-RLHF trust report; Instruction-Tuning-Papers trust report.