Native Reasoning Models: Training Language Models to Reason on Unverifiable Data
NRT model enhances reasoning by self-generating reasoning paths without external verifiers, significantly improving complex reasoning tasks.
Key Findings
Methodology
NRT (Native Reasoning Training) treats the reasoning process as a latent variable, generating reasoning paths using standard question-answer pairs to optimize the probability of producing the correct answer. It does not rely on external verifiers and employs a unified training objective, analyzing failure modes like policy collapse and designing robust reward aggregation functions, forming a self-reinforcing feedback loop.
Key Results
- NRT achieved state-of-the-art performance among verifier-free methods on Llama and Mistral models, significantly outperforming standard SFT baselines and prior verifier-free RL methods. It excels in complex reasoning domains, showing high robustness to policy collapse.
- On the GSM8K dataset, NRT-WS (-log p) boosted the 8B model's score from 29.0 to 76.0, far surpassing RLPR's 65.0.
- NRT-GM improved the BBH score to 54.3, a substantial 13.1 points over RLPR, demonstrating latent reasoning skills for complex, multi-step problems.
Significance
NRT significantly enhances performance on complex reasoning tasks without external verifiers, reducing data costs and expanding the applicability of reasoning training. By self-generating reasoning paths, NRT overcomes the limitations of traditional methods that rely on high-quality human-annotated data and external verifiers, providing a general, scalable path toward building more powerful and broadly applicable reasoning systems.
Technical Contribution
NRT introduces new reward aggregation functions and training objectives by treating the reasoning process as a latent variable, overcoming issues like policy collapse. Unlike existing SFT and RLVR methods, NRT does not rely on external verifiers and can be applied to a broader range of tasks, especially in unverifiable domains.
Novelty
NRT is the first to optimize reasoning as a latent variable, differing from traditional methods that rely on external verifiers. Its innovation lies in self-generating reasoning paths to resolve model uncertainty, forming a self-reinforcing learning mechanism.
Limitations
- NRT may perform poorly on tasks requiring explicit verification due to the lack of external verifiers.
- The initial model capacity might affect the final performance as it relies on self-generated reasoning paths.
Future Work
Future research could explore NRT's application in more domains, such as open-ended question answering and creative writing. Further optimization of reward aggregation functions and training objectives could enhance reasoning capability and stability.
AI Executive Summary
Current large reasoning models rely on high-quality human-annotated data and external verifiers, increasing data collection costs and embedding human cognitive biases. To overcome these limitations, this paper introduces the NRT (Native Reasoning Training) framework, which cultivates complex reasoning by generating reasoning paths using only standard question-answer pairs. NRT treats the reasoning process as a latent variable and employs a unified training objective, rewarding paths that increase the model's likelihood of producing the correct answer.
Empirical results show that NRT achieves state-of-the-art performance among verifier-free methods on Llama and Mistral models, significantly outperforming standard SFT baselines and prior verifier-free RL methods. It excels in complex reasoning domains, showing high robustness to policy collapse. Notably, on the GSM8K dataset, NRT-WS (-log p) boosted the 8B model's score from 29.0 to 76.0, far surpassing RLPR's 65.0.
NRT's success lies in its reward aggregation function design, guiding the model to generate reasoning paths that resolve its own uncertainty. By prioritizing tokens the model finds difficult, NRT-WS (-log p) becomes the top-performing method overall. However, NRT may perform poorly on tasks requiring explicit verification due to the lack of external verifiers. Future research could explore NRT's application in more domains, such as open-ended question answering and creative writing.
Deep Analysis
Background
In recent years, large language models (LLMs) have demonstrated remarkable abilities in complex tasks such as mathematics, coding, and multi-step problem-solving. The prevailing approach combines Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR), but these methods rely on high-quality human-annotated data and external verifiers, limiting their application in unverifiable tasks.
Core Problem
Traditional methods are limited by their reliance on high-quality human-annotated data and external verifiers, leading to high data collection costs and embedding human cognitive biases. Additionally, the RLVR stage is confined to objectively assessable domains like mathematics and coding, leaving a wide range of unverifiable tasks beyond its scope.
Innovation
NRT introduces a new framework by treating the reasoning process as a latent variable, generating reasoning paths using standard question-answer pairs. Its core innovation is in self-generating reasoning paths to resolve model uncertainty, forming a self-reinforcing learning mechanism. Unlike traditional methods, NRT does not rely on external verifiers and can be applied to a broader range of tasks.
Methodology
- �� NRT treats the reasoning process as a latent variable, generating reasoning paths using standard question-answer pairs.
- �� Employs a unified training objective, rewarding paths that increase the model's likelihood of producing the correct answer.
- �� Analyzes failure modes like policy collapse and designs robust reward aggregation functions.
- �� Forms a self-reinforcing feedback loop where the model learns to generate reasoning paths that resolve its own uncertainty.
Experiments
Experiments were conducted on Llama and Mistral models using a 200K sample from the tulu-3-sft-mixture dataset. All methods started from the same untuned checkpoint to ensure fair comparison. Evaluation benchmarks included BBH, MMLU, and GSM8K, trained using the GRPO algorithm with a learning rate of 1e-5 and a batch size of 256.
Results
NRT achieved state-of-the-art performance among verifier-free methods on Llama and Mistral models, significantly outperforming standard SFT baselines and prior verifier-free RL methods. On the GSM8K dataset, NRT-WS (-log p) boosted the 8B model's score from 29.0 to 76.0, far surpassing RLPR's 65.0.
Applications
NRT is applicable to unverifiable tasks such as open-ended question answering and creative writing. Its lack of reliance on external verifiers reduces data costs and expands the applicability of reasoning training.
Limitations & Outlook
NRT may perform poorly on tasks requiring explicit verification due to the lack of external verifiers. The initial model capacity might affect the final performance as it relies on self-generated reasoning paths. Future research could explore NRT's application in more domains.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Traditional methods are like needing a detailed recipe and chef guidance to make a delicious dish, while NRT is like a smart chef who only needs to know the ingredients and desired taste to figure out the best cooking method. NRT improves its cooking skills through self-trial and adjustment, without needing external detailed guidance.
ELI14 Explained like you're 14
Imagine you're playing a puzzle game. Traditional methods are like needing a walkthrough to pass a level, while NRT is like a smart player who only needs to know the game's goal to figure out the best strategy. NRT improves its gaming skills through constant trial and adjustment, without needing external detailed guidance. Isn't that cool?
Glossary
Native Reasoning Training (NRT)
A method that enhances reasoning by self-generating reasoning paths without external verifiers.
The core method proposed in this paper for solving unverifiable reasoning tasks.
Supervised Fine-Tuning (SFT)
A method that fine-tunes a pre-trained model by imitating expert demonstrations.
The first stage of traditional reasoning model training.
Reinforcement Learning with Verifiable Rewards (RLVR)
A reinforcement learning method that confirms the correctness of a model's output using an external verifier.
The second stage of traditional reasoning model training.
Policy Collapse
A phenomenon where the policy converges to simple, low-entropy outputs, reducing exploration.
NRT avoids this by designing robust reward aggregation functions.
Weighted Sum (WS)
A reward aggregation strategy that prioritizes tokens the model finds difficult.
A reward mechanism used in NRT to enhance reasoning path quality.
Open Questions Unanswered questions from this research
- 1 How to further improve NRT's performance on tasks requiring explicit verification without external verifiers?
- 2 How to optimize NRT's reward aggregation functions to better resolve model uncertainty?
Applications
Immediate Applications
Open-ended Question Answering
NRT can be used for open-ended question answering, reducing data costs and improving reasoning capability.
Long-term Vision
Creative Writing
NRT has potential in creative writing by enhancing creation quality through self-generated reasoning paths.
Abstract
The prevailing paradigm for training large reasoning models--combining Supervised Fine-Tuning (SFT) with Reinforcement Learning with Verifiable Rewards (RLVR)--is fundamentally constrained by its reliance on high-quality, human-annotated reasoning data and external verifiers. This dependency incurs significant data-collection costs, risks embedding human cognitive biases, and confines the reinforcement learning stage to objectively assessable domains like mathematics and coding, leaving a wide range of unverifiable tasks beyond its scope. To overcome these limitations, we introduce NRT (Native Reasoning Training), a novel framework that cultivates complex reasoning by having the model generate its own reasoning traces using only standard question-answer pairs, thereby obviating the need for expert-written demonstrations. NRT reframes the training problem by treating the reasoning process as a latent variable. It employs a unified training objective that models reasoning as an optimization problem, intrinsically rewarding paths that increase the model's likelihood of producing the ground-truth answer. This unified perspective allows us to analyze intrinsic failure modes of prior methods, such as policy collapse, and systematically design more robust reward aggregation functions, creating a self-reinforcing feedback loop where the model learns to think in ways that resolve its own uncertainty. Empirical evaluation on Llama and Mistral model families demonstrates that NRT achieves state-of-the-art performance among verifier-free methods, significantly outperforming standard SFT baselines and prior verifier-free RL methods. Our approach yields particularly strong performance gains in complex reasoning domains and exhibits high robustness to policy collapse, offering a general, scalable path toward building more powerful and broadly applicable reasoning systems.