---
title: "lerobot vs awesome-RLHF"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/huggingface-lerobot-vs-opendilab-awesome-rlhf"
tools: ["huggingface-lerobot", "opendilab-awesome-rlhf"]
---

# lerobot vs awesome-RLHF

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick lerobot when tags unique to lerobot: end-to-end learning, robotics; pick awesome-RLHF when requirements: The language used in awesome-RLHF is unknown but given its focus on modern ML projects, familiarity with key languages like Python and frameworks such as PyToro; Continuous updates mean users need to adapt their workflows to integrate the latest resources and methodologies.

[lerobot](https://huggingface.co/docs/lerobot) reports 26k GitHub stars, 5.1k forks, and 887 open issues, last pushed Jul 11, 2026. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) has 4.4k stars, 255 forks, and 4 open issues, last pushed May 20, 2026. Figures are from public GitHub metadata via [lerobot's repository](https://github.com/huggingface/lerobot) and [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF).

| | [lerobot](/tools/huggingface-lerobot.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) |
| --- | --- | --- |
| Tagline | Making AI for Robotics more accessible with end-to-end learning | A curated list of reinforcement learning with human feedback resources (continually updated) |
| Stars | 25,714 | 4,411 |
| Forks | 5,053 | 255 |
| Open issues | 887 | 4 |
| Language | Python | - |
| Adopt for | - | awesome-RLHF is a curated list of resources for Reinforcement Learning with Human Feedback (RLHF) that focuses on applications in large language models. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | Apache-2.0 license |
| Categories | Model Training, Developer Tools | Developer Tools |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [lerobot](/tools/huggingface-lerobot.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Steady (60%) |
| Days since push | 0d | 51d |
| Open issues (now) | 887 | 4 |
| Full report | [trust report](/tools/huggingface-lerobot/trust.md) | [trust report](/tools/opendilab-awesome-rlhf/trust.md) |

## Decision facts: awesome-RLHF

- **Requirements:** The language used in awesome-RLHF is unknown but given its focus on modern ML projects, familiarity with key languages like Python and frameworks such as PyToro; Continuous updates mean users need to adapt their workflows to integrate the latest resources and methodologies
- **Adopt for:** awesome-RLHF is a curated list of resources for Reinforcement Learning with Human Feedback (RLHF) that focuses on applications in large language models.
- **License detail:** Apache-2.0 license

## Choose when

### Choose lerobot if…

- Tags unique to lerobot: end-to-end learning, robotics.
- Also covers Model Training.
- More GitHub stars (26k vs 4.4k) - visibility, not fit.

### Choose awesome-RLHF if…

- Requirements: The language used in awesome-RLHF is unknown but given its focus on modern ML projects, familiarity with key languages like Python and frameworks such as PyToro; Continuous updates mean users need to adapt their workflows to integrate the latest resources and methodologies.
- Tags unique to awesome-RLHF: reinforcement-learning, deep-learning, rlhf, large-language-models.
- When your project involves training large language models using human feedback to refine reinforcement learning processes.

## When NOT to use lerobot

- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Developer Tools: A gateway is overkill when you're pinned to a single provider and model.

## When NOT to use awesome-RLHF

- For scenarios requiring real-time decision support systems where immediate action is necessary, as RLHF usually requires iterative cycles of training and feedback.
- In situations where the model does not benefit significantly from human feedback, such as when dealing with well-defined environments or simple reinforcement learning tasks without complex human input

## Common questions

### What is the difference between lerobot and awesome-RLHF?

lerobot: Making AI for Robotics more accessible with end-to-end learning. 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 lerobot over awesome-RLHF?

Choose lerobot over awesome-RLHF when Tags unique to lerobot: end-to-end learning, robotics; Also covers Model Training; More GitHub stars (26k vs 4.4k) - visibility, not fit.

### When should I choose awesome-RLHF over lerobot?

Choose awesome-RLHF over lerobot when Requirements: The language used in awesome-RLHF is unknown but given its focus on modern ML projects, familiarity with key languages like Python and frameworks such as PyToro; Continuous updates mean users need to adapt their workflows to integrate the latest resources and methodologies; Tags unique to awesome-RLHF: reinforcement-learning, deep-learning, rlhf, large-language-models; When your project involves training large language models using human feedback to refine reinforcement learning processes.

### When should I avoid lerobot?

Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Developer Tools: A gateway is overkill when you're pinned to a single provider and model.

### When should I avoid awesome-RLHF?

For scenarios requiring real-time decision support systems where immediate action is necessary, as RLHF usually requires iterative cycles of training and feedback. In situations where the model does not benefit significantly from human feedback, such as when dealing with well-defined environments or simple reinforcement learning tasks without complex human input

### Is lerobot or awesome-RLHF more popular on GitHub?

lerobot has more GitHub stars (25,714 vs 4,411). Stars measure visibility, not whether either tool fits your constraints.

### Are lerobot and awesome-RLHF open source?

Yes - both are open-source projects on GitHub (lerobot: Apache-2.0, awesome-RLHF: Apache-2.0).

### Where can I find alternatives to lerobot or awesome-RLHF?

GraphCanon lists graph-backed alternatives at [lerobot alternatives](/tools/huggingface-lerobot/alternatives) and [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) ([lerobot markdown twin](/tools/huggingface-lerobot/alternatives.md), [awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/alternatives.md)), 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](/compare/huggingface-lerobot-vs-opendilab-awesome-rlhf.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, lerobot or awesome-RLHF?

lerobot: Very active. 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 lerobot and awesome-RLHF?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lerobot trust report](/tools/huggingface-lerobot/trust); [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=huggingface-lerobot`](/api/graphcanon/graph?tool=huggingface-lerobot)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_