---
title: "ROLL vs LLM-Agent-Paper-List"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/alibaba-roll-vs-woooodyy-llm-agent-paper-list"
tools: ["alibaba-roll", "woooodyy-llm-agent-paper-list"]
---

# ROLL vs LLM-Agent-Paper-List

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick ROLL when tags unique to ROLL: agentic, rlhf, rlvr; pick LLM-Agent-Paper-List when tags unique to LLM-Agent-Paper-List: agent, large-language-models, llm, nlp.

[ROLL](https://alibaba.github.io/ROLL/) reports 3.3k GitHub stars, 295 forks, and 119 open issues, last pushed Jul 11, 2026. [LLM-Agent-Paper-List](https://arxiv.org/abs/2309.07864) has 8.2k stars, 492 forks, and 25 open issues, last pushed Sep 12, 2025. Figures are from public GitHub metadata via [ROLL's repository](https://github.com/alibaba/ROLL) and [LLM-Agent-Paper-List's repository](https://github.com/WooooDyy/LLM-Agent-Paper-List).

| | [ROLL](/tools/alibaba-roll.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) |
| --- | --- | --- |
| Tagline | Efficient and user-friendly scaling library for RL with LLMs | The paper list of the 86-page SCIS cover paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al. |
| Stars | 3,292 | 8,159 |
| Forks | 295 | 492 |
| Open issues | 119 | 25 |
| Language | Python | - |
| Adopt for | - | LLM-Agent-Paper-List is a meticulously curated collection focused on essential research papers related to AI agents built using Large Language Models, encompassing reinforcement learning frameworks and methodologies. It’ |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | - |
| Categories | Evaluation & Observability, Model Training | AI Agents, LLM Frameworks, Model Training |

## Trust and health

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

| | [ROLL](/tools/alibaba-roll.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 0d | 302d |
| Open issues (now) | 119 | 25 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/alibaba-roll/trust.md) | [trust report](/tools/woooodyy-llm-agent-paper-list/trust.md) |

## Decision facts: LLM-Agent-Paper-List

- **Adopt for:** LLM-Agent-Paper-List is a meticulously curated collection focused on essential research papers related to AI agents built using Large Language Models, encompassing reinforcement learning frameworks and methodologies. It’

## Choose when

### Choose ROLL if…

- Tags unique to ROLL: agentic, rlhf, rlvr.
- Also covers Evaluation & Observability.
- More recently updated (last pushed Jul 11, 2026).

### Choose LLM-Agent-Paper-List if…

- Tags unique to LLM-Agent-Paper-List: agent, large-language-models, llm, nlp.
- Also covers AI Agents, LLM Frameworks.
- Ideal for researchers and developers deeply interested in the theory and applications of LLM-based agents.

## When NOT to use ROLL

- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

## When NOT to use LLM-Agent-Paper-List

- Not suitable if your focus is on traditional machine learning models without a language model foundation.
- May not be as helpful if you are looking for resources outside the scope of large language model-based agents, such as purely statistical modeling techniques.

## Common questions

### What is the difference between ROLL and LLM-Agent-Paper-List?

ROLL: Efficient and user-friendly scaling library for RL with LLMs. LLM-Agent-Paper-List: The paper list of the 86-page SCIS cover paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.. See the comparison table for live GitHub stats and shared categories.

### When should I choose ROLL over LLM-Agent-Paper-List?

Choose ROLL over LLM-Agent-Paper-List when Tags unique to ROLL: agentic, rlhf, rlvr; Also covers Evaluation & Observability; More recently updated (last pushed Jul 11, 2026).

### When should I choose LLM-Agent-Paper-List over ROLL?

Choose LLM-Agent-Paper-List over ROLL when Tags unique to LLM-Agent-Paper-List: agent, large-language-models, llm, nlp; Also covers AI Agents, LLM Frameworks; Ideal for researchers and developers deeply interested in the theory and applications of LLM-based agents.

### When should I avoid ROLL?

Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

### When should I avoid LLM-Agent-Paper-List?

Not suitable if your focus is on traditional machine learning models without a language model foundation. May not be as helpful if you are looking for resources outside the scope of large language model-based agents, such as purely statistical modeling techniques.

### Is ROLL or LLM-Agent-Paper-List more popular on GitHub?

LLM-Agent-Paper-List has more GitHub stars (8,159 vs 3,292). Stars measure visibility, not whether either tool fits your constraints.

### Are ROLL and LLM-Agent-Paper-List open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to ROLL or LLM-Agent-Paper-List?

GraphCanon lists graph-backed alternatives at [ROLL alternatives](/tools/alibaba-roll/alternatives) and [LLM-Agent-Paper-List alternatives](/tools/woooodyy-llm-agent-paper-list/alternatives) ([ROLL markdown twin](/tools/alibaba-roll/alternatives.md), [LLM-Agent-Paper-List markdown twin](/tools/woooodyy-llm-agent-paper-list/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/alibaba-roll-vs-woooodyy-llm-agent-paper-list.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, ROLL or LLM-Agent-Paper-List?

ROLL: Very active. LLM-Agent-Paper-List: Slowing. 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 ROLL and LLM-Agent-Paper-List?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ROLL trust report](/tools/alibaba-roll/trust); [LLM-Agent-Paper-List trust report](/tools/woooodyy-llm-agent-paper-list/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=alibaba-roll`](/api/graphcanon/graph?tool=alibaba-roll)
- 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/_