Comparison
ColossalAI vs CodeRL
Verdict
Pick ColossalAI when license: ColossalAI is Apache-2.0, CodeRL is BSD-3-Clause; pick CodeRL when license: CodeRL is BSD-3-Clause, ColossalAI is Apache-2.0.
Markdown twin · ColossalAI alternatives · CodeRL alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | ColossalAI | CodeRL |
|---|---|---|
| Maintenance | Steady (46d since push) As of today · github_public_v1 | Steady (39d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | 29 low (29 low) As of today · osv@v1 |
Tagline
- ColossalAI
- Making large AI models cheaper, faster and more accessible
- CodeRL
- This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22).
Stars
- ColossalAI
- 41k
- CodeRL
- 572
Forks
- ColossalAI
- 4.5k
- CodeRL
- 68
Open issues
- ColossalAI
- 501
- CodeRL
- 42
Language
- ColossalAI
- Python
- CodeRL
- Python
Adopt for
- ColossalAI
- ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models.
- CodeRL
- -
Persona
- ColossalAI
- -
- CodeRL
- -
Runtime
- ColossalAI
- -
- CodeRL
- -
License
- ColossalAI
- Apache-2.0
- CodeRL
- BSD-3-Clause
Last pushed
- ColossalAI
- May 25, 2026
- CodeRL
- Jun 2, 2026
Categories
- ColossalAI
- Model Training, Inference & Serving
- CodeRL
- Model Training, Evaluation & Observability
Trust and health
Days since push
- ColossalAI
- 46d
- CodeRL
- 39d
Open issues (now)
- ColossalAI
- 501
- CodeRL
- 42
Security scan
- ColossalAI
- No lockfile
- CodeRL
- 29 low (29 low)
Full report
- ColossalAI
- Trust report
- CodeRL
- Trust report
Shared compatibility
- Python · ColossalAI: Python runtime · CodeRL: Python runtime
Choose ColossalAI if…
- License: ColossalAI is Apache-2.0, CodeRL is BSD-3-Clause.
- Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models.
- Also covers Inference & Serving.
- You require handling extremely large AI models with massive context windows, such as over 2M tokens.
When NOT to use ColossalAI
- You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems.
- Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series).
- You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7.
Choose CodeRL if…
- License: CodeRL is BSD-3-Clause, ColossalAI is Apache-2.0.
- Tags unique to CodeRL: reinforcementlearning, programsynthesis, machinelearning, python.
- Also covers Evaluation & Observability.
When NOT to use CodeRL
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hpcaitech/ColossalAI) · observed Jul 11, 2026
- GitHub forks (hpcaitech/ColossalAI) · observed Jul 11, 2026
- Last push (hpcaitech/ColossalAI) · observed May 25, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (salesforce/CodeRL) · observed Jul 11, 2026
- GitHub forks (salesforce/CodeRL) · observed Jul 11, 2026
- Last push (salesforce/CodeRL) · observed Jun 2, 2026
- License file (BSD-3-Clause) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ColossalAI 41k · CodeRL 572 (synced Jul 11, 2026).
Common questions
- What is the difference between ColossalAI and CodeRL?
- ColossalAI: Making large AI models cheaper, faster and more accessible. CodeRL: This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22).. See the comparison table for live GitHub stats and shared categories.
- When should I choose ColossalAI over CodeRL?
- Choose ColossalAI over CodeRL when License: ColossalAI is Apache-2.0, CodeRL is BSD-3-Clause; Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens.
- When should I choose CodeRL over ColossalAI?
- Choose CodeRL over ColossalAI when License: CodeRL is BSD-3-Clause, ColossalAI is Apache-2.0; Tags unique to CodeRL: reinforcementlearning, programsynthesis, machinelearning, python; Also covers Evaluation & Observability.
- When should I avoid ColossalAI?
- You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7.
- When should I avoid CodeRL?
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- Is ColossalAI or CodeRL more popular on GitHub?
- ColossalAI has more GitHub stars (41,408 vs 572). Stars measure visibility, not whether either tool fits your constraints.
- Are ColossalAI and CodeRL open source?
- Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, CodeRL: BSD-3-Clause).
- Where can I find alternatives to ColossalAI or CodeRL?
- GraphCanon lists graph-backed alternatives at ColossalAI alternatives and CodeRL alternatives (ColossalAI markdown twin, CodeRL 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, ColossalAI or CodeRL?
- ColossalAI: Steady. CodeRL: 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 ColossalAI and CodeRL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ColossalAI trust report; CodeRL trust report.