Hybrid Reward Normalization for Process-supervised Non-verifiable Agentic Tasks
Introduces Hybrid Reward Normalization (PPR) achieving state-of-the-art performance across benchmarks.
Key Findings
Methodology
The paper introduces a Reinforcement Learning framework called Principle Process Reward (PPR), combining principle-based process evaluation and outcome verification. PPR trains a principle-driven reward model to enhance transparency and reliability of process evaluation and introduces a Reward Normalization strategy to calibrate outcome and process rewards.
Key Results
- PPR shows outstanding performance across benchmarks, achieving a 28% average improvement on datasets like NQ, TriviaQA, and PopQA.
- Compared to existing RL methods, PPR improves result accuracy by 15%.
- On the non-verifiable benchmark NVProcessBench, PPRM achieves an accuracy of 0.613.
Significance
PPR addresses the sparse signal problem in traditional RL methods for long trajectory tasks by combining process and outcome rewards. It has broad potential applications in academia and industry, especially for tasks requiring complex reasoning and tool usage.
Technical Contribution
PPR provides a new Reward Normalization strategy addressing stability issues when integrating process and outcome rewards. The principle-driven process evaluation model enhances transparency and reliability of process supervision.
Novelty
PPR is the first to combine principle-driven process evaluation with outcome verification, offering a novel Reward Normalization method that significantly enhances RL performance in complex tasks.
Limitations
- In some non-verifiable tasks, annotating process rewards remains challenging, potentially affecting model generalization.
- The performance of the Reward Normalization strategy in extremely long trajectory tasks requires further validation.
Future Work
Future research directions include extending PPR to more diverse task scenarios and optimizing the Reward Normalization strategy for longer task trajectories.
AI Executive Summary
Large Language Models (LLMs) increasingly rely on external tools like search engines to solve complex tasks. However, traditional reinforcement learning methods face sparse signal issues in long trajectory tasks, limiting model performance. This paper introduces a new reinforcement learning framework called Principle Process Reward (PPR), which combines principle-based process evaluation and outcome verification. PPR trains a principle-driven reward model to enhance transparency and reliability of process evaluation and introduces a Reward Normalization strategy to calibrate outcome and process rewards.
Experimental results show that PPR achieves state-of-the-art performance across multiple benchmarks, with a 28% average improvement on datasets like NQ, TriviaQA, and PopQA. Additionally, PPR achieves an accuracy of 0.613 on the non-verifiable benchmark NVProcessBench, demonstrating its robustness and generalization in complex tasks.
While PPR excels in many aspects, annotating process rewards in some non-verifiable tasks remains challenging, potentially affecting model generalization. Future research directions include extending PPR to more diverse task scenarios and optimizing the Reward Normalization strategy for longer task trajectories.
Deep Analysis
Background
In recent years, Large Language Models (LLMs) have made significant progress in tasks like open-domain question answering and multi-step reasoning. By integrating with external tools like search engines, LLMs can access verifiable and up-to-date knowledge, reducing hallucinations. However, training LLMs to effectively leverage these tools remains challenging, especially in task decomposition, query generation, and information aggregation.
Core Problem
Traditional reinforcement learning methods face sparse signal issues in long trajectory tasks, limiting model performance. Existing methods primarily rely on outcome rewards for final answers, lacking precise supervision for intermediate steps, leading to credit assignment difficulties.
Innovation
The proposed Principle Process Reward (PPR) addresses the sparse signal problem in traditional RL methods for long trajectory tasks by combining principle-based process evaluation and outcome verification. PPR introduces a new Reward Normalization strategy addressing stability issues when integrating process and outcome rewards.
Methodology
- �� Develop a principle-driven process reward model (PPRM) based on a set of predefined principles for process evaluation.
- �� Introduce a Reward Normalization strategy (ReNorm) to unify and calibrate outcome and process rewards.
- �� Optimize using Generalized Advantage Estimation (GAE) and Proximal Policy Optimization (PPO).
Experiments
Experiments were conducted on multiple benchmarks, including NQ, TriviaQA, and PopQA. Exact Match (EM) was used as the evaluation metric, and ablation studies were conducted to verify the effectiveness of PPR.
Results
PPR shows outstanding performance across benchmarks, achieving a 28% average improvement on datasets like NQ, TriviaQA, and PopQA. Compared to existing RL methods, PPR improves result accuracy by 15%.
Applications
PPR can be applied to tasks requiring complex reasoning and tool usage, such as open-domain question answering and multi-step reasoning. Its principle-driven process evaluation enhances transparency and reliability of process supervision.
Limitations & Outlook
In some non-verifiable tasks, annotating process rewards remains challenging, potentially affecting model generalization. The performance of the Reward Normalization strategy in extremely long trajectory tasks requires further validation.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Traditionally, you only taste the dish at the end to judge its success, but this might miss issues in intermediate steps. PPR is like an experienced chef who tastes and adjusts at each step, ensuring each step meets standards. This way, even in complex dishes, success is guaranteed.
ELI14 Explained like you're 14
Imagine you're playing a game that requires many steps to complete. Usually, you only know how well you did at the end, but that's hard to improve. PPR is like a game assistant that gives you feedback at each step, telling you what's good and what needs improvement. This way, you get better at each step and eventually win the game!
Glossary
Reinforcement Learning
A machine learning method that trains models through rewards and penalties to perform better in specific tasks.
Used to train LLMs to effectively leverage tools in complex tasks.
Principle Process Reward
A reward strategy combining principle-based process evaluation and outcome verification, enhancing transparency and reliability of process supervision.
PPR addresses the sparse signal problem.
Reward Normalization
A method to calibrate outcome and process rewards, ensuring stability and consistency of reward signals.
Used to integrate process and outcome rewards.
Generalized Advantage Estimation
A technique for estimating policy advantage, helping improve RL algorithm stability.
Used to optimize policies in PPR.
Proximal Policy Optimization
A reinforcement learning algorithm that improves training stability by limiting policy update magnitude.
Used to optimize policies in PPR.
Open Questions Unanswered questions from this research
- 1 How to further optimize the Reward Normalization strategy in extremely long trajectory tasks to enhance model stability and performance.
- 2 How to effectively annotate process rewards in non-verifiable tasks to improve model generalization.
Applications
Immediate Applications
Open-domain Question Answering
PPR can improve the accuracy of open-domain QA systems, especially in scenarios requiring complex reasoning.
Long-term Vision
Complex Task Automation
PPR has the potential to automate decision-making processes in complex tasks, improving efficiency and accuracy.
Abstract
Large Language Models (LLMs) increasingly rely on external tools such as search engines to solve complex agentic tasks that require reasoning and external knowledge retrieval. Recently, reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in advancing capabilities of LLMs by rewarding the final answers via outcome rewards. While straightforward to supervise, outcome rewards only provide sparse signals and delayed feedback, which limits their effectiveness on long trajectories. Process rewards address this by evaluating intermediate steps, providing fine-grained supervision and encouraging grounded problem solving. However, it is notoriously hard to annotate step-wise labels, especially in non-verifiable process without "golden" answers. Furthermore, step-wise judgment requires the balance between local quality with contribution to the final outcome, as optimizing towards higher process reward may not always align with better final outcomes. To address the above challenges, we introduce Principle Process Reward (PPR), an RL approach that unifies principled step-level assessment and outcome verification. We train a principle-based reward model to improve the transparency and reliability of process evaluation, and further introduce a Reward Normalization (ReNorm) strategy to calibrate outcome and process rewards. Experiment results show that PPR achieves state-of-the-art performance across a wide range of benchmarks, demonstrating its impressive robustness and generalization. Our code and model collection is available in this link.