benchmax 0.1.2.dev27

Framework-Agnostic RL Environments for LLM Fine-Tuning

benchmax: Framework-Agnostic RL Environments for LLM Fine-Tuning

A lightweight, training-framework agnostic library for defining, running, and parallelizing environments, to fine-tune OSS LLMs with reinforcement learning.

📌 News

• [29 Oct 2025] 🎉 Added support for easy multi-node parallelization across all major cloud providers using SkyPilot

ℹ️ Overview

benchmax comes with:

• A collection of ready-to-use reinforcement learning (RL) environments for LLM fine-tuning ranging from multi-hop search to spreadsheet manipulation to CRM agents
• An easy way to define, compose, and parallelize your own environments, including leveraging the existing ecosystem of MCP servers
• Trainer-agnostic by design — BaseEnv exposes a small async interface (list_tools, run_tool, compute_reward, plus optional rollout lifecycle hooks) that any rollout loop can drive
• Optional batteries-included add-ons: synthetic RAG dataset generation (benchmax[rag]), agent trace import (benchmax[traces]), and clients for the Castform training platform (benchmax.platform)

Define your environment as:

1. A toolset (LLM calls, external APIs, calculators, MCPs, etc.).
2. Output parsing logic to extract structured observations.
3. Reward functions to score model outputs.

Rollout management, parallel execution, etc. comes out of the box.

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💡 Core Features

Built-in examples & templates

Get started with ready to use recipes, from Wikipedia search to spreadsheet manipulation. Easy to copy, customize, and extend. And yes, more are on the way.

MCP support

Tap into the growing MCP ecosystem and integrate them as tools within your environments.

Multi-node parallel execution

Multi-node parallelization enabled out of the box with state isolation across roll-outs (e.g. editing files on filesystem, etc.).

🌐 Creating Environments

What is an environment?

An environment consists of:

• A list of tools that an LLM can call
• A list of reward functions that evaluate the quality & correctness of the model's final output.

We also support MCP servers natively, allowing you to easily leverage the many servers built by the community.

Pre-built environments

Ready-to-use environments with pre-configured tools and reward functions.

• CRM
• Excel
• Math
• Wikipedia
• PostgreSQL search (benchmax[rag])

How do I create a custom environment?

1. With existing MCP servers (Built-in support for multi-node parallelization)

2. Extend BaseEnv

How about more complex environments?

• Check out our excel spreadsheet RL environment: benchmax.envs.excel.excel_env.ExcelEnv

I want a specific environment

Open an issue and tag us & we will look into building you one!

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🎯 Motivation

• Modularity and Simplicity:

  We set out to build a lightweight, modular system for defining RL environments—breaking them down into simple, composable parts: tools, tool output parsing, and reward functions.

  The goal's to make it easy for software engineers to build and experiment with RL environments without needing deep RL expertise.

• Task Recipes and Ideas:

  We want benchmax to be a living library of reusable, RL-compatible task recipes, ready to inspire and extend beyond the usual suspects like math and coding. We aim to support more real-world workflows, including open-ended and long-horizon tasks.

• Parallelization and Cloud Compatibility:

  • Enable efficient parallelization with maintained statefulness between rollouts.
  • Facilitate easy deployment and scalability in cloud environments.

• MCP as a first class citizen:

  There has been an explosion of MCP servers/tools built out for use-cases ranging from browser use to excel to game creation. benchmax allows folks to leverage and compose these existing MCP servers to build environments integrated with real world systems e.g. excel

🤝 Contributing

We welcome new environment recipes and bug reports!

⭐ Star our repository to show your support!

📦 Add-ons

In addition to the core env library, benchmax ships several optional modules behind extras:

Extra	Module	Purpose
benchmax[rag]	benchmax.rag.*	Markdown chunking, corpus indexing (Postgres / Chroma / Pinecone / Turbopuffer), synthetic QA dataset generation, RAG-specific reward rubrics
benchmax[traces]	benchmax.traces	Agentic trace import (Braintrust today, Langfuse coming) and provider-agnostic processing pipeline
benchmax[chroma] / [pinecone] / [turbopuffer]	benchmax.rag.corpus.*	Corpus-backend pins (combine with [rag])
(core)	benchmax.platform	HTTP clients for the Castform platform — storage uploads, training-job launch, rollout server. Used both internally by benchmax.rag and by the high-level castform-sdk.

All platform URLs derive from CASTFORM_BASE_DOMAIN (default castform.com) with per-component overrides; see benchmax.config.

📜 License

Apache 2.0 © 2026 Castform