Best RLHF Tools for OpenAI
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Compare the Top RLHF Tools that integrate with OpenAI as of June 2025
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This a list of RLHF tools that integrate with OpenAI. Use the filters on the left to add additional filters for products that have integrations with OpenAI. View the products that work with OpenAI in the table below.
What are RLHF Tools for OpenAI?
Reinforcement Learning from Human Feedback (RLHF) tools are used to fine-tune AI models by incorporating human preferences into the training process. These tools leverage reinforcement learning algorithms, such as Proximal Policy Optimization (PPO), to adjust model outputs based on human-labeled rewards. By training models to align with human values, RLHF improves response quality, reduces harmful biases, and enhances user experience. Common applications include chatbot alignment, content moderation, and ethical AI development. RLHF tools typically involve data collection interfaces, reward models, and reinforcement learning frameworks to iteratively refine AI behavior. Compare and read user reviews of the best RLHF tools for OpenAI currently available using the table below. This list is updated regularly.
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Lamini
Lamini
Lamini makes it possible for enterprises to turn proprietary data into the next generation of LLM capabilities, by offering a platform for in-house software teams to uplevel to OpenAI-level AI teams and to build within the security of their existing infrastructure. Guaranteed structured output with optimized JSON decoding. Photographic memory through retrieval-augmented fine-tuning. Improve accuracy, and dramatically reduce hallucinations. Highly parallelized inference for large batch inference. Parameter-efficient finetuning that scales to millions of production adapters. Lamini is the only company that enables enterprise companies to safely and quickly develop and control their own LLMs anywhere. It brings several of the latest technologies and research to bear that was able to make ChatGPT from GPT-3, as well as Github Copilot from Codex. These include, among others, fine-tuning, RLHF, retrieval-augmented training, data augmentation, and GPU optimization.Starting Price: $99 per month -
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Gymnasium
Gymnasium
Gymnasium is a maintained fork of OpenAI’s Gym library, providing a standard API for reinforcement learning and a diverse collection of reference environments. The Gymnasium interface is simple, pythonic, and capable of representing general RL problems, and has a compatibility wrapper for old Gym environments. At the core of Gymnasium is the Env class, a high-level Python class representing a Markov Decision Process (MDP) from reinforcement learning theory. The class provides users the ability to generate an initial state, transition to new states given an action, and visualize the environment. Alongside Env, Wrapper classes are provided to help augment or modify the environment, particularly the agent observations, rewards, and actions taken. Gymnasium includes various built-in environments and utilities to simplify researchers’ work, along with being supported by most training libraries.
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