Best AI tools for< Perform Reinforcement Learning >

DeepSeek R1 is a revolutionary open-source AI model for advanced reasoning that outperforms leading AI models in mathematics, coding, and general reasoning tasks. It utilizes a sophisticated MoE architecture with 37B active/671B total parameters and 128K context length, incorporating advanced reinforcement learning techniques. DeepSeek R1 offers multiple variants and distilled models optimized for complex problem-solving, multilingual understanding, and production-grade code generation. It provides cost-effective pricing compared to competitors like OpenAI o1, making it an attractive choice for developers and enterprises.

AdGen AI is an AI-powered creative generator that helps businesses create high-performing ad copy and visuals for multiple ad channels. It uses machine learning models to analyze product data and generate a variety of ad creatives that are tailored to the target audience. AdGen AI also allows users to publish ads directly from the platform, making it easy to launch and manage ad campaigns.

JobInterview.guru is an AI-powered platform designed to provide personalized interview training for job seekers. Leveraging advanced AI technology, the platform offers realistic job interview simulations, detailed insights into interview questions, and personalized feedback to help users prepare effectively. With a focus on efficiency and cost-effectiveness, JobInterview.guru aims to empower users to confidently navigate their job interviews and land their dream jobs.

LambdaTest is a next-generation mobile apps and cross-browser testing cloud platform that offers a wide range of testing services. It allows users to perform manual live-interactive cross-browser testing, run Selenium, Cypress, Playwright scripts on cloud-based infrastructure, and execute AI-powered automation testing. The platform also provides accessibility testing, real devices cloud, visual regression cloud, and AI-powered test analytics. LambdaTest is trusted by over 2 million users globally and offers a unified digital experience testing cloud to accelerate go-to-market strategies.

Laxis is a revolutionary AI Meeting Assistant designed to capture and distill key insights from every customer interaction effortlessly. It seamlessly integrates across platforms, from online meetings to CRM updates, all with a user-friendly interface. Laxis empowers revenue teams to maximize every customer conversation, ensuring no valuable detail is missed. With Laxis, sales teams can close more deals with AI note-taking and insights from client conversations, business development teams can engage prospects more effectively and grow their business faster, marketing teams can repurpose podcasts, webinars, and meetings into engaging content with a single click, product and market researchers can conduct better research interviews that get to the "aha!" moment faster, project managers can remember key takeaways and status updates, and capture them for progress reports, and product and UX designers can capture and organize insights from their interviews and user research.

CampaignBuilder.AI is an AI-powered platform that enables users to quickly generate and launch AI-optimized advertising campaigns across major ad platforms. The tool offers a range of features to streamline campaign creation, including AI-generated copywriting, audience targeting, and creative building. With full-funnel capabilities, CampaignBuilder.AI aims to help businesses of all sizes improve campaign performance and efficiency. The platform provides users with creative freedom and automation to save time and enhance campaign effectiveness.

Laxis is an AI Meeting Assistant designed to empower revenue teams by capturing and distilling key insights from customer interactions effortlessly. It offers seamless integration across platforms, from online meetings to CRM updates, with a user-friendly interface. Laxis helps users stay focused during meetings, auto-generate meeting summaries, identify customer requirements, and extract valuable insights. It supports multilingual interactions, real-time transcriptions, and provides answers based on past conversations. Trusted by over 35,000 business professionals from 3000 organizations, Laxis saves time, improves note-taking, and enhances communication with clients and prospects.

Ask Blue J is a generative AI tool designed specifically for tax experts. It provides fast, verifiable answers to complex tax questions, helping professionals work smarter and more efficiently. With its extensive database of curated tax content and industry-leading AI technology, Ask Blue J enables users to conduct efficient research, expedite drafting, and enhance their overall productivity.

Blue J is a legal technology company founded in 2015, dedicated to enhancing tax research with the power of AI. Their AI-powered tool, Ask Blue J, provides fast and verifiable answers to tax questions, enabling tax professionals to work more efficiently. Blue J's generative AI technology helps users find authoritative sources quickly, expedite drafting processes, and cater to junior staff's research needs. The tool is trusted by hundreds of leading firms and offers a comprehensive database of curated tax content.

Sales Closer AI is an AI-powered sales tool designed to help businesses scale their sales operations by creating AI agents capable of handling various tasks such as phone calls, scheduling, and conducting personalized discovery calls. The tool integrates seamlessly with existing CRM and marketing tools, enabling users to uncover customer pain points, build rapport, and deliver interactive demos in multiple languages. Sales Closer AI continuously learns and optimizes its approach, providing detailed notes for future reference and boosting conversion rates across different industries.

GPTConsole is an AI-powered platform that helps developers build production-ready applications faster and more efficiently. Its AI agents can generate code for a variety of applications, including web applications, AI applications, and landing pages. GPTConsole also offers a range of features to help developers build and maintain their applications, including an AI agent that can learn your entire codebase and answer your questions, and a CLI tool for accessing agents directly from the command line.

The website is currently displaying a '403 Forbidden' error, which means that access to the requested resource is forbidden. This error is typically caused by insufficient permissions or server misconfiguration. The 'openresty' message indicates that the website is using the OpenResty web platform, which is based on NGINX and Lua programming language. OpenResty is known for its high performance and scalability in handling web traffic and serving dynamic content.

Validator by Yazero is a platform that helps users validate their startup ideas using AI. It provides a community where users can share their ideas, get feedback, and find collaborators. Validator also offers a variety of features to help users improve their ideas, such as idea validation, market research, and financial planning.

pdfAssistant is a powerful AI chatbot designed to assist users with various PDF processing tasks. It offers a user-friendly chat-based interface that allows users to convert, watermark, merge, split, and perform other PDF-related operations using natural language commands. The application is powered by industry-leading PDF and AI technology, providing fast and accurate results. With pdfAssistant, users can work smarter and more efficiently by simplifying complex PDF software processes.

KYP.ai is a productivity intelligence platform that offers a 360° view of organizations across people, process, and technology dimensions. It provides instant productivity intelligence, end-to-end process optimization, holistic productivity insights, ROI-driven automation, and unparalleled scalability. The platform helps in live visibility, immediate impact, hybrid workplace management, technology landscape rationalization, and AI-powered aggregation and analysis. KYP.ai focuses on workforce enablement, no integration hassles, no-code configuration, and secure, privacy-compliant data processing.

Solidroad is an AI-first training and feedback platform that turns company knowledge-base into immersive training programs. It offers personalized coaching, realistic simulations, and real-time feedback to improve team performance. The platform aims to make training programs easier to manage and more engaging for employees.

Vizly is an AI-powered data analysis tool that empowers users to make the most of their data. It allows users to chat with their data, visualize insights, and perform complex analysis. Vizly supports various file formats like CSV, Excel, and JSON, making it versatile for different data sources. The tool is free to use for up to 10 messages per month and offers a student discount of 50%. Vizly is suitable for individuals, students, academics, and organizations looking to gain actionable insights from their data.

Yogger is an AI-powered video analysis and movement assessment tool designed for coaches, trainers, physical therapists, and athletes. It allows users to track form, gather data, and analyze movement for any sport or activity in seconds. With AI-powered screenings, users can get instant scores and insights whether training in person or online. Yogger's software solutions help in recovery, enhance training, and prevent injuries, all accessible from a phone. The tool offers features like precision movement analysis, joint tracking with skeleton visualization, range of motion tracking, drawing tools for analysis, and virtual assessments for streamlined client evaluations.

GeekSight Inc. offers a range of Trello Power-Ups designed to enhance team collaboration and productivity. One of their key products is Notes & Docs, an AI-powered note-taking tool integrated with Trello. The tool aims to streamline task management by providing comprehensive task context information organization and content creation workflows. Additionally, they provide PersonaTool for building customer personas and Card Annotations for enhancing communication on Trello boards.

Dreamwriter is an AI-powered content creation tool that allows users to design beautiful, on-brand premium content in minutes. By leveraging the power of AI and the user's brand voice, Dreamwriter helps in developing hard-hitting PDFs & PPTs tailored to the exact target audience. The tool features an intuitive UI editor, real-time collaboration, simplified daily content generation, and the ability to write in multiple languages. Dreamwriter aims to streamline the content creation process by providing a toolbox of leading solutions to produce premium content at unprecedented speeds.

R1-searcher is a tool designed to incentivize the search capability in large reasoning models (LRMs) via reinforcement learning. It enables LRMs to invoke web search and obtain external information during the reasoning process by utilizing a two-stage outcome-supervision reinforcement learning approach. The tool does not require instruction fine-tuning for cold start and is compatible with existing Base LLMs or Chat LLMs. It includes training code, inference code, model checkpoints, and a detailed technical report.

Pai-Megatron-Patch is a deep learning training toolkit built for developers to train and predict LLMs & VLMs by using Megatron framework easily. With the continuous development of LLMs, the model structure and scale are rapidly evolving. Although these models can be conveniently manufactured using Transformers or DeepSpeed training framework, the training efficiency is comparably low. This phenomenon becomes even severer when the model scale exceeds 10 billion. The primary objective of Pai-Megatron-Patch is to effectively utilize the computational power of GPUs for LLM. This tool allows convenient training of commonly used LLM with all the accelerating techniques provided by Megatron-LM.

AI Robotics tutorials for hobbyists focusing on training a virtual humanoid/robot to perform football tricks using Reinforcement Learning. The tutorials cover tuning training hyperparameters, optimizing reward functions, and achieving results in a short training time without the need for GPUs.

Agentq is a tool that utilizes various agentic architectures to complete tasks on the web reliably. It includes a planner-navigator multi-agent architecture, a solo planner-actor agent, an actor-critic multi-agent architecture, and an actor-critic architecture with reinforcement learning and DPO finetuning. The repository also contains an open-source implementation of the research paper 'Agent Q'. Users can set up the tool by installing dependencies, starting Chrome in dev mode, and setting up necessary environment variables. The tool can be run to perform various tasks related to autonomous AI agents.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework enables LLMs to reason and interact with the environment, optimizing entire trajectories for long-horizon reasoning while maintaining computational efficiency.

AReaL (Ant Reasoning RL) is an open-source reinforcement learning system developed at the RL Lab, Ant Research. It is designed for training Large Reasoning Models (LRMs) in a fully open and inclusive manner. AReaL provides reproducible experiments for 1.5B and 7B LRMs, showcasing its scalability and performance across diverse computational budgets. The system follows an iterative training process to enhance model performance, with a focus on mathematical reasoning tasks. AReaL is equipped to adapt to different computational resource settings, enabling users to easily configure and launch training trials. Future plans include support for advanced models, optimizations for distributed training, and exploring research topics to enhance LRMs' reasoning capabilities.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework consists of MDP formulation, RICO algorithm with rollout and update stages, and reward normalization strategies to stabilize training. RAGEN aims to optimize reasoning and action strategies for LLM agents operating in complex environments.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Humanoid-Gym is a reinforcement learning framework designed for training locomotion skills for humanoid robots, focusing on zero-shot transfer from simulation to real-world environments. It integrates a sim-to-sim framework from Isaac Gym to Mujoco for verifying trained policies in different physical simulations. The codebase is verified with RobotEra's XBot-S and XBot-L humanoid robots. It offers comprehensive training guidelines, step-by-step configuration instructions, and execution scripts for easy deployment. The sim2sim support allows transferring trained policies to accurate simulated environments. The upcoming features include Denoising World Model Learning and Dexterous Hand Manipulation. Installation and usage guides are provided along with examples for training PPO policies and sim-to-sim transformations. The code structure includes environment and configuration files, with instructions on adding new environments. Troubleshooting tips are provided for common issues, along with a citation and acknowledgment section.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

AI-Toolbox is a C++ library aimed at representing and solving common AI problems, with a focus on MDPs, POMDPs, and related algorithms. It provides an easy-to-use interface that is extensible to many problems while maintaining readable code. The toolbox includes tutorials for beginners in reinforcement learning and offers Python bindings for seamless integration. It features utilities for combinatorics, polytopes, linear programming, sampling, distributions, statistics, belief updating, data structures, logging, seeding, and more. Additionally, it supports bandit/normal games, single agent MDP/stochastic games, single agent POMDP, and factored/joint multi-agent scenarios.

AceCoder is a tool that introduces a fully automated pipeline for synthesizing large-scale reliable tests used for reward model training and reinforcement learning in the coding scenario. It curates datasets, trains reward models, and performs RL training to improve coding abilities of language models. The tool aims to unlock the potential of RL training for code generation models and push the boundaries of LLM's coding abilities.

This repository contains code for training reward models for Deep Reinforcement Learning-based Reward-modulated Hierarchical Fine-tuning (DRL-based RLHF), Iterative Selection Fine-tuning (Rejection sampling fine-tuning), and iterative Decision Policy Optimization (DPO). The reward models are trained using a Bradley-Terry model based on the Gemma and Mistral language models. The resulting reward models achieve state-of-the-art performance on the RewardBench leaderboard for reward models with base models of up to 13B parameters.

mLoRA (Multi-LoRA Fine-Tune) is an open-source framework for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. It allows concurrent fine-tuning of multiple LoRA adapters with a shared base model, efficient pipeline parallelism algorithm, support for various LoRA variant algorithms, and reinforcement learning preference alignment algorithms. mLoRA helps save computational and memory resources when training multiple adapters simultaneously, achieving high performance on consumer hardware.

The 'understand-r1-zero' repository focuses on understanding R1-Zero-like training from a critical perspective. It provides insights into base models and reinforcement learning components, highlighting findings and proposing solutions for biased optimization. The repository offers a minimalist recipe for R1-Zero training, detailing the RL-tuning process and achieving state-of-the-art performance with minimal compute resources. It includes codebase, models, and paper related to R1-Zero training implemented with the Oat framework, emphasizing research-friendly and efficient LLM RL techniques.

AutoWebGLM is a project focused on developing a language model-driven automated web navigation agent. It extends the capabilities of the ChatGLM3-6B model to navigate the web more efficiently and address real-world browsing challenges. The project includes features such as an HTML simplification algorithm, hybrid human-AI training, reinforcement learning, rejection sampling, and a bilingual web navigation benchmark for testing AI web navigation agents.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

HuatuoGPT-o1 is a medical language model designed for advanced medical reasoning. It can identify mistakes, explore alternative strategies, and refine answers. The model leverages verifiable medical problems and a specialized medical verifier to guide complex reasoning trajectories and enhance reasoning through reinforcement learning. The repository provides access to models, data, and code for HuatuoGPT-o1, allowing users to deploy the model for medical reasoning tasks.

Notte is a web browser designed specifically for LLM agents, providing a language-first web navigation experience without the need for DOM/HTML parsing. It transforms websites into structured, navigable maps described in natural language, enabling users to interact with the web using natural language commands. By simplifying browser complexity, Notte allows LLM policies to focus on conversational reasoning and planning, reducing token usage, costs, and latency. The tool supports various language model providers and offers a reinforcement learning style action space and controls for full navigation control.

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