Reinforcing Thinking through Reasoning-Enhanced Reward Models (2501.01457v1)

Abstract: LLMs exhibit great potential in complex multi-step reasoning through inference-time thinking but still struggle with deciding when to stop thinking due to limited self-awareness about their knowledge boundaries. While human preference alignment has shown extraordinary opportunities, expensive labeling challenges adherence to scaling law. LLM self-critique, as an alternative to using human-labeled reasoning data, is questioned with its inherited biases. This work addresses these challenges by distilling the LLM's own reasoning processes into synthetic behavioral data, eliminating the need for manual labeling of intermediate steps. Building on this concept, we propose Distillation-Reinforcement-Reasoning (DRR), a three-step framework that leverages the LLM's inherent behaviors as external feedback by first generating behavioral data using the Reasoner (LLM) to reflect its reasoning capabilities, then training a lightweight discriminative reward model (DM) on behavioral data, and finally deploying the DM at inference time to assist the Reasoner's decision-making. Experiments on multiple benchmarks show that the DRR framework outperforms self-critique approaches without relying on additional complex data annotation. Benefiting from lightweight design, ease of replication, and adaptability, DRR is applicable to a wide range of LLM-centric tasks.

• The paper introduces the Distillation-Reinforcement-Reasoning (DRR) framework, which enhances LLM multi-step reasoning using synthetic behavioral data and a discriminative reward model.
• The trained discriminative model provides real-time feedback during inference, allowing the LLM to dynamically refine its reasoning process without altering model parameters.
• This approach offers a cost-effective and scalable way to improve LLM reasoning by leveraging behavioral data, outperforming existing self-critique methods and applying to various LLM architectures.

Reinforcing Thinking through Reasoning-Enhanced Reward Models

The paper "Reinforcing Thinking through Reasoning-Enhanced Reward Models" presents a novel framework designed to enhance the multi-step reasoning capabilities of LLMs. LLMs, though remarkable in numerous natural language processing tasks, face persistent challenges in deciding when to conclude reasoning processes, often due to limited self-awareness concerning their own knowledge boundaries. This paper introduces a structured approach known as the Distillation-Reinforcement-Reasoning (DRR) framework, which aims to address these challenges by effectively augmenting the reasoning capabilities of LLMs through an innovative use of synthetic behavioral data.

Methodology and Contributions

The DRR framework consists of three primary components:

1. Reasoning Process Distillation: This approach leverages the LLM itself to generate synthetic behavioral data without requiring human-labeled intermediate steps. The process involves the LLM generating an answer and rationale, evaluated against a ground truth, where incorrect responses lead to further iterative reasoning. This method offers a scalable means of collecting diverse data that mirrors real-world LLM reasoning scenarios.
2. Discriminative Model (DM) Training: A lightweight discriminative reward model is trained on the synthetic behavioral data to evaluate whether the outputs of the LLM at each reasoning step should be accepted or further corrected. The model addresses acceptance and rejection with different weights to mitigate the effects of incorrect acceptances, which are costly in terms of reasoning reliability.
3. System Deployment for Inference: The trained DM is deployed alongside the reasoner LLM during inference, providing feedback in real time across multiple reasoning iterations. The DM decides whether the reasoning process should continue or conclude, facilitating adaptive reasoning akin to human problem-solving methodologies.

The crux of this method lies in its ability to enable the LLM to refine its reasoning dynamically, leading to improved decision-making without explicitly altering the model's parameters. The authors demonstrate the efficacy of this system via experiments on several benchmarks, where DRR consistently outperformed existing self-critique approaches.

Implications and Future Work

The research presents noteworthy implications, particularly in the field of AI interpretability and efficiency. By circumventing reliance on expensive annotated datasets and focusing instead on BD methods, DRR promotes a cost-effective path toward refining complex reasoning tasks in LLMs. Its adaptability across various LLM architectures, including closed-source systems like GPT-4, signifies potential for wide applicability.

Moreover, the strategic combination of generative and discriminative strengths offers a robust mechanism for identifying and mitigating errors in reasoning processes. This suggests a promising avenue for further refining the feedback loops inherent in advanced AI systems, potentially aligning them more closely with human cognitive behaviors.

Future research could explore extending DRR to domains beyond textual reasoning, such as visual or multimodal reasoning tasks. Additionally, investigating the integration of newer model architectures or alternative discriminative strategies could offer enhanced performance and deeper insights into the reasoning abilities of AI systems.

Overall, the paper provides a substantial contribution to the field by delineating a pathway to amplify the multi-step reasoning abilities of LLMs through a blend of in-context learning and distillation methods, offering scalable solutions to longstanding challenges in machine reasoning.