Home/Compare/awesome-RLHF vs LLM-Agent-Paper-List

Comparison

awesome-RLHF vs LLM-Agent-Paper-List

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 LLM-Agent-Paper-List if lLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning.

Markdown twin · awesome-RLHF alternatives · LLM-Agent-Paper-List alternatives

GraphCanon updated Sep 20, 2026

12views this month

awesome-RLHF logo

awesome-RLHF

opendilab/awesome-RLHF

★ 4.4kpushed May 20, 2026
vs
LLM-Agent-Paper-List logo

LLM-Agent-Paper-List

WooooDyy/LLM-Agent-Paper-List

★ 8.2kpushed Sep 12, 2025

Trust & integrity

Signalawesome-RLHFLLM-Agent-Paper-List
Maintenance
Steady (89d since push)
As of Aug 17, 2026 · github_public_v1
Dormant (371d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Aug 17, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 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

awesome-RLHF
A curated list of reinforcement learning with human feedback resources (continually updated)
LLM-Agent-Paper-List
Must-read papers for LLM-based agents.

Stars

awesome-RLHF
4.4k
LLM-Agent-Paper-List
8.2k

Forks

awesome-RLHF
258
LLM-Agent-Paper-List
499

Open issues

awesome-RLHF
6
LLM-Agent-Paper-List
32

Language

awesome-RLHF
-
LLM-Agent-Paper-List
-

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.
LLM-Agent-Paper-List
LLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning.

Persona

awesome-RLHF
-
LLM-Agent-Paper-List
-

Runtime

awesome-RLHF
-
LLM-Agent-Paper-List
-

License

awesome-RLHF
Apache-2.0
LLM-Agent-Paper-List
-

Last pushed

awesome-RLHF
May 20, 2026
LLM-Agent-Paper-List
Sep 12, 2025

Categories

awesome-RLHF
Evaluation & Observability, Model Training
LLM-Agent-Paper-List
AI Agents, Evaluation & Observability, Model Training

Trust and health

Maintenance

awesome-RLHF
Steady (60%)
LLM-Agent-Paper-List
Dormant (18%)

Days since push

awesome-RLHF
89d
LLM-Agent-Paper-List
371d

Open issues (now)

awesome-RLHF
6
LLM-Agent-Paper-List
32

Stars delta

awesome-RLHF
+9 (30d)
LLM-Agent-Paper-List
+50 (30d)

Open issues delta

awesome-RLHF
0 (30d)
LLM-Agent-Paper-List
+1 (30d)

Owner type

awesome-RLHF
Organization
LLM-Agent-Paper-List
User

Full report

awesome-RLHF
Trust report
LLM-Agent-Paper-List
Trust report

Choose awesome-RLHF if…

  • Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, rlhf.
  • When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems.
  • More recently updated (last pushed May 20, 2026).

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 LLM-Agent-Paper-List if…

  • Tags unique to LLM-Agent-Paper-List: agent, evaluation, llm, nlp.
  • Also covers AI Agents.
  • When you need a comprehensive list of must-read papers on LLM-based agents, including detailed insights into AgentGym, a framework for developing and training LLM agents through reinforcement learning

When NOT to use LLM-Agent-Paper-List

  • If you are looking for a tool that focuses on a broader range of AI agent development beyond LLM-based agents, as LLM-Agent-Paper-List is specifically curated for LLM-based agents
  • When you need real-time updates or a dynamic list of papers, as the repository is a static list and does not automatically update with new publications
  • If you require a tool that includes practical code implementations for all listed papers, as the repository primarily serves as a list and does not provide comprehensive code for every paper

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: awesome-RLHF 4.4k · LLM-Agent-Paper-List 8.2k (synced Sep 20, 2026).

Common questions

What is the difference between awesome-RLHF and LLM-Agent-Paper-List?
awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). LLM-Agent-Paper-List: Must-read papers for LLM-based agents.. See the comparison table for live GitHub stats and shared categories.
When should I choose awesome-RLHF over LLM-Agent-Paper-List?
Choose awesome-RLHF over LLM-Agent-Paper-List when Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, rlhf; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems; More recently updated (last pushed May 20, 2026).
When should I choose LLM-Agent-Paper-List over awesome-RLHF?
Choose LLM-Agent-Paper-List over awesome-RLHF when Tags unique to LLM-Agent-Paper-List: agent, evaluation, llm, nlp; Also covers AI Agents; When you need a comprehensive list of must-read papers on LLM-based agents, including detailed insights into AgentGym, a framework for developing and training LLM agents through reinforcement learning.
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 LLM-Agent-Paper-List?
If you are looking for a tool that focuses on a broader range of AI agent development beyond LLM-based agents, as LLM-Agent-Paper-List is specifically curated for LLM-based agents When you need real-time updates or a dynamic list of papers, as the repository is a static list and does not automatically update with new publications If you require a tool that includes practical code implementations for all listed papers, as the repository primarily serves as a list and does not provide comprehensive code for every paper
Is awesome-RLHF or LLM-Agent-Paper-List more popular on GitHub?
LLM-Agent-Paper-List has more GitHub stars (8,222 vs 4,422). Stars measure visibility, not whether either tool fits your constraints.
Are awesome-RLHF and LLM-Agent-Paper-List open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to awesome-RLHF or LLM-Agent-Paper-List?
GraphCanon lists graph-backed alternatives at awesome-RLHF alternatives and LLM-Agent-Paper-List alternatives (awesome-RLHF markdown twin, LLM-Agent-Paper-List 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 LLM-Agent-Paper-List?
awesome-RLHF: Steady. LLM-Agent-Paper-List: 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 LLM-Agent-Paper-List?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-RLHF trust report; LLM-Agent-Paper-List trust report.

Was this helpful?

Anonymous feedback helps us improve pages and translations.