PURE
SOTA RL fine-tuning solution for advanced math reasoning of LLM
Stars: 83
PURE (Process-sUpervised Reinforcement lEarning) is a framework that trains a Process Reward Model (PRM) on a dataset and fine-tunes a language model to achieve state-of-the-art mathematical reasoning capabilities. It uses a novel credit assignment method to calculate return and supports multiple reward types. The final model outperforms existing methods with minimal RL data or compute resources, achieving high accuracy on various benchmarks. The tool addresses reward hacking issues and aims to enhance long-range decision-making and reasoning tasks using large language models.
README:
TL;DR: Process Reward Model (PRM) trained on PRM800K dataset can fine-tune LLM to achieve SOTA-level mathematical reasoning capabilities for < $150 (8 A100 GPUs * 16 hours).
- [2025/02/22] 🔥🔥We release the notion blog, which details our algorithm, the difference between gamma-decay and min-form credit assignment, examples of reward hacking, and so on🔥🔥
- [2025/02/09] We release the training, evaluation code, wandb logs, and checkpoints. Paper's on it's way!
This month, we saw a huge boost in LLM reasoning power from the verifiable reward (VR)-based Reinforcement learning fine-tuning (ReFT), like DeepSeek R1, SimpleRL-Zero, and TinyZero. Previous work has encountered challenges and made unsuccessful attempts in exploring PRM, so we wonder: How far can PRM actually take us? How does it stack up against VR-based methods in reasoning performance, training costs?
To answer these questions, we present PURE (Process-sUpervised Reinforcement lEarning). Using Qwen2.5-Math-7B as the base model, we train a PRM on 369k data from the PRM800K dataset, and then fine-tune another Qwen2.5-Math-7B model using only 8K MATH prompts, process rewards from the PRM, and optional verifiable rewards. For the RL algorithm, we use the PPO loss with an RLOO advantage estimator. We improve credit assignment by using a weighted sum of the process rewards, $\sum_t \text{softmax}(-\text{PR}_t/T)\cdot\text{PR}_t$ which approximates ${\min}_t \text{PR}_t$ when $T\rightarrow 0$, instead of the usual gamma decay sum $\sum_t \gamma^t \cdot \text{PR}_t$ to calculate return. Our framework supports multiple reward types: only process reward (PURE-PRM), only verifiable reward (PURE-VR) which is the Deepseek-R1-zero's setting, or a mix of both (PURE-PRM+VR), as shown in the following table.
📊 The final model achieves pass@1 accuracy of 82.6% on MATH500, 82.5% on AMC, and 53.3% on average across 5 benchmarks, beating Qwen2.5-math-7B-instruct, PRIME, and SimpleRL with just either <1/50th RL data or 1/5th of the compute resources.
All results are in pass@1 accuracy
| AIME 2024 | MATH 500 | AMC | Minerva Math | OlympiadBench | Avg. | |
|---|---|---|---|---|---|---|
| Qwen2.5-Math-7B-Base | 13.3 | 71.8 | 47.5 | 29.8 | 35.1 | 39.5 |
| Qwen-2.5-Math-7B-Instruct | 16.7 | 83.2 | 52.5 | 37.5 | 41.3 | 46.2 |
| Eurus-2-7B-PRIME | 26.7 | 79.2 | 57.8 | 38.6 | 42.1 | 48.9 |
| Qwen2.5-7B-SimpleRL-Zero | 33.3 | 77.2 | 62.5 | 33.5 | 37.6 | 48.8 |
| Qwen2.5-7B-PURE-PRM+VR* | 20.0 | 82.6 | 82.5 | 37.1 | 44.1 | 53.3 |
| Qwen2.5-7B-PURE-PRM | 16.7 | 81.8 | 60.0 | 38.2 | 44.7 | 49.3 |
| Qwen2.5-7B-PURE-VR | 23.3 | 79.4 | 60.0 | 36.8 | 41.8 | 48.3 |
*The SOTA model was trained using 8K MATH problems, of which only ~800 gave ground-truth final answers that could be used to calculate VRs.
Note: Eurus-2-7B-PRIME, and SimpleRL-Zero are also based on Qwen-2.5-Math-7B.
Our code is implemented based on OpenRLHF. Please follow OpenRLHF's guidance to configure required environments. Then run pip install -r requirements.txt
We train the PRM in 2 stages using TRL and a preprocessed PRM800K dataset. In the first stage, we freeze the LLM and only train the last score layer (MLP) with 1e-4 learning rate rate for 3 epochs. In the second stage, we unfreeze the LLM and fine-tune all parameters with 1e-6 learning rate for 1 epoch. The resultant PRM is released through HuggingFace.
cd PRM
# stage 1
bash train_stage_1.sh
# stage 2
bash train_stage_2.shWe evaluate our PRM using BoN method, ProcessBench, and PRMBench.
- For BoN, we use the data from RLHFlow. With $N=1024$ answers generated by Deepseek-7B for each question, we use our PRM to calculate process rewards and then aggregate them into an outcome reward for each answer. The answer with the highest outcome reward is selected as the final answer. Our PRM achieves pass@1024 scores of 91.6% on GSM8K and 62.6% on MATH, compared to RLHFlow's best scores of 93.0% and 58.1%, respectively.
| Generator Model | Method | GSM8K | MATH |
|---|---|---|---|
| Deepseek-7B | Pass@1 | 83.9 | 42.4 |
| Deepseek-7B | Majority Voting@1024 | 89.7 | 57.4 |
| Deepseek-7B | Deepseek-PRM@1024 | 93.0 | 58.1 |
| Deepseek-7B | Our-PRM@1024 | 91.6 | 62.6 |
- On ProcessBench, which tests the PRM's ability to identify the first process error, our PRM scores an average F1 of 57.5, outperforming the best PRM's F1 score of 56.5 in ProcessBench.
| Process Reward Model | GSM8K | MATH | OlympiadBench | OmniMATH | Average |
|---|---|---|---|---|---|
| Qwen2.5-Math-7B-PRM800K | 68.2 | 62.6 | 50.7 | 44.3 | 56.5 |
| Our PRM-7B | 69.0 | 66.5 | 48.4 | 45.9 | 57.5 |
- On PRMBench, which is designed to assess the fine-grained error detection capabilities of PRMs, our PRM gets an overall score of 65.3, ranking 🥉third among open source PRMs. You can find our PRM named
Pure-PRM-7Bon the official leaderboard.
These results confirm that our PRM is SOTA and suitable for fine-tuning LLMs.
To start training, run the following command. It uses Ray+vLLM for rollout acceleration, with the first 4 GPUs allocated for the actor, initial actor (reference model), and PRM. The remaining GPUs are used for the vLLM engines. This setup works with 5 to 8 GPUs—just adjust the number of vLLM engines in the script accordingly.
bash examples/scripts/train_pure.shWe used Qwen Math's codebase for evaluation (i.e., pass@1 accuracy). For fairness considerations, we completely prohibited solving problems by calling code, following SimpleRL. Please follow the /eval instructions for evaluation.
Reward hacking often occurs when relying solely on process rewards from the PRM, typically marked by sudden, extreme changes in metrics like reward, KL divergence, and loss. Meanwhile, the model starts only generating irrelevant outputs like "thanks" or "happy birthday" without any other string related to the questions. Since PRM is trained and inferences causally, such outputs can yield positive process scores, even though they are meaningless for math reasoning. You can see examples of this in the wandb log with run name starting with "PRM_".
In our experiments, reward hacking usually happened within the first 200 steps. However, before this occurs, the model performs well. For example, the Qwen2.5-7B-PURE-PRM model shown in the table above is saved at step 100, before hacking began.
Another factor that can trigger reward hacking is the baseline choice in RLOO. One intuitive way is to use the average process reward per step from other answers in the group as the baseline. However, this setting favors answers with fewer steps (refer to this issue for details). Since we split steps using a specific character (i.e., "\n\n"), we find the model sometimes avoids this character, producing answers with fewer steps but excessively long tokens per step. The PRM struggles to assign accurate process rewards to such lengthy steps. To address this, we change the baseline to the average reward per token from other answers, multiplied by the number of tokens in the current step. This improvement penalizes steps with more tokens more heavily and removes the bias toward fewer steps.
Unfortunately, we did not observe the aha moment, self-reflection, or long CoT for schemes using PRM. We suppose that even if an answer like "<response A> Wait, wait. <response B>" is generated in the rollout, the PRM will assign negative process rewards to response A and positive process rewards to response B. The PPO algorithm then probably decrease the sampling probability of response A, and increase the sampling probability of response B, resulting in the final model that just outputs response B and thus no aha moment.
- [ ] paper with more discussions and evaluations
- [ ] attempts to mitigate reward hacking for PRM (Online PURE).
If you find our code useful, we would appreciate it if you could cite our work:
@misc{cheng2025pure,
title={Stop Gamma Decay: Min-Form Credit Assignment Is All Process Reward Model Needs for Reasoning},
author={Jie Cheng and Lijun Li and Gang Xiong and Jing Shao and Yisheng Lv and Fei-Yue Wang},
year={2025},
howpublished={\url{https://tungsten-ink-510.notion.site/Stop-Gamma-Decay-Min-Form-Credit-Assignment-Is-All-Process-Reward-Model-Needs-for-Reasoning-19fcb6ed0184804eb07fd310b38af155?pvs=4}},
note={Notion Blog}
year={2025}
}We implement our RL algorithm based on OpenRLHF. We thank the developers of OpenRLHF and the author of SimpleRL for discussion! In addition, we also refer to TRL, PRIME's code and hyperparameter values to varying degrees. Thank them for their wonderful work!
Random Thoughts
I’m very happy about DeepSeek's great success and extremely grateful for their selflessly open-sourced models. Their success has also brought a tremendous level of attention and expectations to VR-based ReFT methods. However, I still want to look back and see where exactly the PRM path can take us. As Tim Berners-Lee once said, "We need diversity of thought in the world to face new challenges." Perhaps, when the conversation becomes longer or the number of steps increases, VR/ORM-based ReFT methods may underperform due to sparse rewards or credit assignment issues?
Since late '23, I've wanted to use LLMs to solve long-range decision-making and reasoning tasks. I tried to play chess using Qwen and Llama, but it was too difficult for their abilities at that time (and still is, even now), and I saw no hope of success with ReFT using such base policies. In mid-'24, I aimed to use an open-sourced model to solve GitHub issues, which is what SWE-Bench evaluated. I was able to obtain a good base policy through prompt engineering, but the conversations were too long, coupled with very sparse rewards, so I still couldn’t get ReFT to work. Now, with sufficient datasets and sufficiently capable base models for mathematical reasoning tasks, I’m finally able to implement the idea I had two years ago. I’m grateful to the open-source community for giving me the opportunity to achieve this goal!
This project took me about a week, and there are still many imperfections. In the future, in the paper, we plan to include more experiments to comprehensively discuss the differences between the PRM and VR approaches. I hope you can understand and sympathize with the current limitations.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for PURE
Similar Open Source Tools
PURE
PURE (Process-sUpervised Reinforcement lEarning) is a framework that trains a Process Reward Model (PRM) on a dataset and fine-tunes a language model to achieve state-of-the-art mathematical reasoning capabilities. It uses a novel credit assignment method to calculate return and supports multiple reward types. The final model outperforms existing methods with minimal RL data or compute resources, achieving high accuracy on various benchmarks. The tool addresses reward hacking issues and aims to enhance long-range decision-making and reasoning tasks using large language models.
AReaL
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.
uptrain
UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases), perform root cause analysis on failure cases and give insights on how to resolve them.
nesa
Nesa is a tool that allows users to run on-prem AI for a fraction of the cost through a blind API. It provides blind privacy, zero latency on protected inference, wide model coverage, cost savings compared to cloud and on-prem AI, RAG support, and ChatGPT compatibility. Nesa achieves blind AI through Equivariant Encryption (EE), a new security technology that provides complete inference encryption with no additional latency. EE allows users to perform inference on neural networks without exposing the underlying data, preserving data privacy and security.
Equivariant-Encryption-for-AI
At Nesa, privacy is a critical objective. Equivariant Encryption (EE) is a solution developed to perform inference on neural networks without exposing input and output data. EE integrates specialized transformations for neural networks, maintaining data privacy while ensuring inference operates correctly on encrypted inputs. It provides the same latency as plaintext inference with no slowdowns and offers strong security guarantees. EE avoids the computational costs of traditional Homomorphic Encryption (HE) by preserving non-linear neural functions. The tool is designed for modern neural architectures, ensuring accuracy, scalability, and compatibility with existing pipelines.
OREAL
OREAL is a reinforcement learning framework designed for mathematical reasoning tasks, aiming to achieve optimal performance through outcome reward-based learning. The framework utilizes behavior cloning, reshaping rewards, and token-level reward models to address challenges in sparse rewards and partial correctness. OREAL has achieved significant results, with a 7B model reaching 94.0 pass@1 accuracy on MATH-500 and surpassing previous 32B models. The tool provides training tutorials and Hugging Face model repositories for easy access and implementation.
fms-fsdp
The 'fms-fsdp' repository is a companion to the Foundation Model Stack, providing a (pre)training example to efficiently train FMS models, specifically Llama2, using native PyTorch features like FSDP for training and SDPA implementation of Flash attention v2. It focuses on leveraging FSDP for training efficiently, not as an end-to-end framework. The repo benchmarks training throughput on different GPUs, shares strategies, and provides installation and training instructions. It trained a model on IBM curated data achieving high efficiency and performance metrics.
rag-cookbooks
Welcome to the comprehensive collection of advanced + agentic Retrieval-Augmented Generation (RAG) techniques. This repository covers the most effective advanced + agentic RAG techniques with clear implementations and explanations. It aims to provide a helpful resource for researchers and developers looking to use advanced RAG techniques in their projects, offering ready-to-use implementations and guidance on evaluation methods. The RAG framework addresses limitations of Large Language Models by using external documents for in-context learning, ensuring contextually relevant and accurate responses. The repository includes detailed descriptions of various RAG techniques, tools used, and implementation guidance for each technique.
SeaLLMs
SeaLLMs are a family of language models optimized for Southeast Asian (SEA) languages. They were pre-trained from Llama-2, on a tailored publicly-available dataset, which comprises texts in Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. The SeaLLM-chat underwent supervised finetuning (SFT) and specialized self-preferencing DPO using a mix of public instruction data and a small number of queries used by SEA language native speakers in natural settings, which **adapt to the local cultural norms, customs, styles and laws in these areas**. SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform **ChatGPT-3.5** in non-Latin languages, such as Thai, Khmer, Lao, and Burmese.
PurpleLlama
Purple Llama is an umbrella project that aims to provide tools and evaluations to support responsible development and usage of generative AI models. It encompasses components for cybersecurity and input/output safeguards, with plans to expand in the future. The project emphasizes a collaborative approach, borrowing the concept of purple teaming from cybersecurity, to address potential risks and challenges posed by generative AI. Components within Purple Llama are licensed permissively to foster community collaboration and standardize the development of trust and safety tools for generative AI.
model2vec
Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance. It outperforms other static embedding models like GLoVe and BPEmb, is lightweight with only `numpy` as a major dependency, offers fast inference, dataset-free distillation, and is integrated into Sentence Transformers, txtai, and Chonkie. Model2Vec creates powerful models by passing a vocabulary through a sentence transformer model, reducing dimensionality using PCA, and weighting embeddings using zipf weighting. Users can distill their own models or use pre-trained models from the HuggingFace hub. Evaluation can be done using the provided evaluation package. Model2Vec is licensed under MIT.
hass-ollama-conversation
The Ollama Conversation integration adds a conversation agent powered by Ollama in Home Assistant. This agent can be used in automations to query information provided by Home Assistant about your house, including areas, devices, and their states. Users can install the integration via HACS and configure settings such as API timeout, model selection, context size, maximum tokens, and other parameters to fine-tune the responses generated by the AI language model. Contributions to the project are welcome, and discussions can be held on the Home Assistant Community platform.
agentUniverse
agentUniverse is a multi-agent framework based on large language models, providing flexible capabilities for building individual agents. It focuses on collaborative pattern components to solve problems in various fields and integrates domain experience. The framework supports LLM model integration and offers various pattern components like PEER and DOE. Users can easily configure models and set up agents for tasks. agentUniverse aims to assist developers and enterprises in constructing domain-expert-level intelligent agents for seamless collaboration.
peft
PEFT (Parameter-Efficient Fine-Tuning) is a collection of state-of-the-art methods that enable efficient adaptation of large pretrained models to various downstream applications. By only fine-tuning a small number of extra model parameters instead of all the model's parameters, PEFT significantly decreases the computational and storage costs while achieving performance comparable to fully fine-tuned models.
agentUniverse
agentUniverse is a multi-agent framework based on large language models, providing flexible capabilities for building individual agents. It focuses on multi-agent collaborative patterns, integrating domain experience to help agents solve problems in various fields. The framework includes pattern components like PEER and DOE for event interpretation, industry analysis, and financial report generation. It offers features for agent construction, multi-agent collaboration, and domain expertise integration, aiming to create intelligent applications with professional know-how.
venice
Venice is a derived data storage platform, providing the following characteristics: 1. High throughput asynchronous ingestion from batch and streaming sources (e.g. Hadoop and Samza). 2. Low latency online reads via remote queries or in-process caching. 3. Active-active replication between regions with CRDT-based conflict resolution. 4. Multi-cluster support within each region with operator-driven cluster assignment. 5. Multi-tenancy, horizontal scalability and elasticity within each cluster. The above makes Venice particularly suitable as the stateful component backing a Feature Store, such as Feathr. AI applications feed the output of their ML training jobs into Venice and then query the data for use during online inference workloads.
For similar tasks
PURE
PURE (Process-sUpervised Reinforcement lEarning) is a framework that trains a Process Reward Model (PRM) on a dataset and fine-tunes a language model to achieve state-of-the-art mathematical reasoning capabilities. It uses a novel credit assignment method to calculate return and supports multiple reward types. The final model outperforms existing methods with minimal RL data or compute resources, achieving high accuracy on various benchmarks. The tool addresses reward hacking issues and aims to enhance long-range decision-making and reasoning tasks using large language models.
For similar jobs
weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.
LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.
kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.
tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.
spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.
Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.