verl-agent

★ 2.4k

code-act

★ 1.7k

verl-agent vs code-act

Q: Which is better, verl-agent or code-act?

By GitHub stars, verl-agent has more community adoption, but the best choice depends on your specific use case.

verl-agent: `verl-agent` extends veRL to train LLM agents using reinforcement learning, featuring a novel step-independent multi-turn rollout mechanism. This design ensures high scalability for long-horizon tasks by allowing customizable per-step input structures and memory management.; code-act: CodeAct unifies LLM agents' actions into an executable code space, enabling dynamic revision and new actions based on execution results. This approach significantly outperforms traditional text and JSON action methods, improving LLM agent success rates on complex tasks.

TL;DR

verl-agentLLM Infra · ★ 2.4k

Training large language model agents for complex multi-turn, long-horizon tasks.

Read verl-agent full review →

code-actLLM Infra · ★ 1.7k

Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments.

Read code-act full review →

Side-by-Side Comparison

Field

verl-agent

code-act

Features

verl-agent

01Multi-Turn Agent-Environment Interaction

02Fully Customizable Memory Module & Per-Step Input Structure

03Scalable for Very Long-Horizon Optimization

04Parallelized Gym-Style Environments and Group Environments

05Diverse Reinforcement Learning Algorithms

code-act

01Unified Action Space via Executable Code: Consolidates LLM agents' actions into a unified, executable code space.

02Dynamic Action Revision: Allows LLM agents to dynamically revise prior actions or emit new actions based on real-time observations.

03Integrated Python Interpreter: Seamlessly integrates with a Python interpreter for code execution.

04Superior Performance: Outperforms widely used alternatives like Text and JSON in LLM agent success rates (up to 20% higher).

05Instruction-Tuned Agents (CodeActAgent): Provides pre-trained CodeActAgent models (Mistral, Llama-2) that excel in out-of-domain agent tasks.

Use Cases

verl-agent

↳Training large language model agents for complex multi-turn, long-horizon tasks.

↳Developing reasoning agents for both visual and text-based environments.

↳Solving digital interface control, embodied AI, and search-related challenges.

code-act

↳Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments.

↳Automated Code Execution and Problem Solving: For scenarios requiring LLMs to execute code, debug, and iterate on solutions based on execution feedback.

↳Complex Task Automation: For automating multi-turn, complex tasks that benefit from dynamic action revision and tool use.

FAQ

What is the difference between verl-agent and code-act?

Both verl-agent and code-act are in the LLM Infra category. verl-agent has 2.4k stars, while code-act has 1.7k stars.

Which is better, verl-agent or code-act?

The best choice depends on your use case. Choose verl-agent if Training large language model agents for complex multi-turn, long-horizon tasks., and code-act if Developing Advanced LLM Agents: For researchers and developers aiming to build more capable and robust LLM agents that can interact dynamically with environments..

Is verl-agent free or open source?

Yes, verl-agent is open source on GitHub (Apache-2.0).

Is code-act free or open source?

Yes, code-act is open source on GitHub (MIT).

Cloud Infrastructure

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Alternatives to verl-agent →Alternatives to code-act →verl-agent details →code-act details →

TL;DR

Side-by-Side Comparison

Features

Use Cases

FAQ

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