Comparison
awesome-RLHF vs RLTF
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 RLTF if rLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.
Markdown twin · awesome-RLHF alternatives · RLTF alternatives
GraphCanon updated 6d
vs
Trust & integrity
| Signal | awesome-RLHF | RLTF |
|---|---|---|
| Maintenance | Steady (89d since push) As of 6d · github_public_v1 | Dormant (669d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 6d · 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 | Published findings 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)
- RLTF
- Accepted by Transactions on Machine Learning Research (TMLR)
Stars
- awesome-RLHF
- 4.4k
- RLTF
- 134
Forks
- awesome-RLHF
- 258
- RLTF
- 7
Open issues
- awesome-RLHF
- 6
- RLTF
- 0
Language
- awesome-RLHF
- -
- RLTF
- Python
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.
- RLTF
- RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.
Persona
- awesome-RLHF
- -
- RLTF
- -
Runtime
- awesome-RLHF
- -
- RLTF
- -
License
- awesome-RLHF
- Apache-2.0
- RLTF
- BSD-3-Clause
Last pushed
- awesome-RLHF
- May 20, 2026
- RLTF
- Oct 5, 2024
Categories
- awesome-RLHF
- Evaluation & Observability, Model Training
- RLTF
- Model Training
Trust and health
Maintenance
- awesome-RLHF
- Steady (60%)
- RLTF
- Dormant (18%)
Days since push
- awesome-RLHF
- 89d
- RLTF
- 669d
Open issues (now)
- awesome-RLHF
- 6
- RLTF
- 0
Stars delta
- awesome-RLHF
- +9 (30d)
- RLTF
- Unknown
Open issues delta
- awesome-RLHF
- 0 (30d)
- RLTF
- Unknown
Owner type
- awesome-RLHF
- Organization
- RLTF
- User
OSV dependency advisories
- awesome-RLHF
- No lockfile (source not queried)
- RLTF
- Published findings
Full report
- awesome-RLHF
- Trust report
- RLTF
- Trust report
Choose awesome-RLHF if…
- License: awesome-RLHF is Apache-2.0, RLTF is BSD-3-Clause.
- Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models.
- 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 RLTF if…
- License: RLTF is BSD-3-Clause, awesome-RLHF is Apache-2.0.
- 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.
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 (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 (Zyq-scut/RLTF) · observed Aug 5, 2026
- GitHub forks (Zyq-scut/RLTF) · observed Aug 5, 2026
- Last push (Zyq-scut/RLTF) · observed Oct 5, 2024
- License file (BSD-3-Clause) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-RLHF 4.4k · RLTF 134 (synced Aug 17, 2026).
Common questions
- What is the difference between awesome-RLHF and RLTF?
- awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). RLTF: Accepted by Transactions on Machine Learning Research (TMLR). See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-RLHF over RLTF?
- Choose awesome-RLHF over RLTF when License: awesome-RLHF is Apache-2.0, RLTF is BSD-3-Clause; Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; 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 RLTF over awesome-RLHF?
- Choose RLTF over awesome-RLHF when License: RLTF is BSD-3-Clause, awesome-RLHF is Apache-2.0; 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.
- 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 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 awesome-RLHF or RLTF more popular on GitHub?
- awesome-RLHF has more GitHub stars (4,422 vs 134). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-RLHF and RLTF open source?
- Yes - both are open-source projects on GitHub (awesome-RLHF: Apache-2.0, RLTF: BSD-3-Clause).
- Where can I find alternatives to awesome-RLHF or RLTF?
- GraphCanon lists graph-backed alternatives at awesome-RLHF alternatives and RLTF alternatives (awesome-RLHF 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, awesome-RLHF or RLTF?
- awesome-RLHF: 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 awesome-RLHF and RLTF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-RLHF trust report; RLTF trust report.