How to Make Large Language Models Reason More Intelligently? An In-Depth Exploration of Interleaved Reasoning Technology

In today’s digital age, with the continuous development of artificial intelligence technology, large language models (LLMs) have become an extremely powerful tool, playing a significant role in numerous fields. However, despite their excellent performance in text generation, these models still have limitations when it comes to handling complex reasoning tasks. Today, let’s delve into a technology that can significantly enhance the reasoning capabilities of large language models—interleaved reasoning, and see how it changes the game.

I. The Current Status and Challenges of Reasoning with Large Language Models

Large language models have demonstrated remarkable reasoning capabilities through long chain-of-thought (CoT) in complex tasks. Nevertheless, the traditional “think-answer” paradigm faces two primary issues: one is the excessively long time-to-first-token (TTFT), which severely impacts user experience in real-time interactive applications; the other is that due to the delayed answer generation, models may follow incorrect intermediate steps, leading to inaccurate final answers.

So, is there a way to enable models to provide feedback during the thinking process to avoid these issues? This is where interleaved reasoning technology comes into play.

II. Interleaved Reasoning Technology: Allowing Models to Think and Answer Simultaneously

Interleaved reasoning is a novel training paradigm that uses reinforcement learning (RL) to guide reasoning LLMs to alternate between thinking and answering, effectively addressing the drawbacks of traditional reasoning models.

(i) The Concept of Interleaved Reasoning

When faced with multi-hop questions, instead of completing all reasoning before generating an answer, the model now provides intermediate answers during the reasoning process. This approach mimics human behavior in conversations, offering immediate feedback and making the reasoning path more transparent for users to verify or correct.

For instance, consider the question: “Who directed the film that won the Academy Award for Best Picture five years after the fall of the Berlin Wall?” In the traditional reasoning mode, the model would first complete all reasoning steps and then provide the answer. This process can be time-consuming. With interleaved reasoning, the model gradually outputs intermediate answers during the thinking process. It first identifies that the Berlin Wall fell in 1989, calculates that five years later would be 1994, determines that the film Forrest Gump won the Best Picture award in 1994, and finally concludes that the director was Robert Zemeckis. In this way, users can quickly access key information.

(ii) Training Models for Interleaved Reasoning

1. Multi-hop Problem Decomposition

We regard answering multi-hop questions as a sequence of resolved intermediate steps. Each intermediate answer is a distinct piece of information or partial conclusion that the model confidently derives at a specific reasoning stage. For example, in a mathematical problem, it could be an intermediate calculation result.

2. The Distinction Between Thinking and Answering

From a user experience perspective, thinking is the internal reasoning process of the model, which is not accessible or useful to users. Answering, on the other hand, refers to the generation of public, finalized conclusions by the model. These conclusions may represent partial solutions to the overall problem but are presented as complete intermediate steps that advance the user’s understanding or problem-solving process.

3. The Application of Interleaved Reasoning Templates

During training and inference, we employ specific instruction templates to guide the model. The template uses only two special tags:

and , explicitly asking the model to perform reasoning and provide answers within each tag, respectively. For example: “You are a helpful assistant. When encountering problems, you reason step by step. Within , you conduct reasoning, and within , you share intermediate results. Every time you gain confidence in an intermediate result, follow this pattern: until you reach the final answer.”

III. Training Details for Interleaved Reasoning

In the reinforcement learning training process, we have designed a reward mechanism to guide the behavior of the model.

(i) Rule-Based Reward Mechanism

We have adopted three types of rule-based rewards: format reward, final accuracy reward, and conditional intermediate accuracy reward.

1. Format Reward

This checks whether the model correctly follows the interleaved format and completes the output, including the proper use of tags and the alternation between thinking and answering.

2. Final Accuracy Reward

This evaluates the correctness of the model’s final answer. The reward is only applied when the format is correct, and exact match is used to determine the correctness of the answer.

3. Conditional Intermediate Accuracy Reward

This provides additional rewards for correct intermediate answers but is only applied under specific conditions, such as when the final answer is correct, the output format is valid, and the model demonstrates learning progress in the current training batch.

(ii) The Model’s Rapid Learning Ability for Formats

Experiments have shown that models possess the ability to quickly learn structural formats. The format reward reaches a plateau rapidly during the early stages of training, while the accuracy reward continues to improve. This indicates that models do not face significant difficulties in adhering to styles. The key challenge lies in enhancing the quality of the model’s reasoning process for different reasoning tasks.

IV. Experimental Verification: The Advantages of Interleaved Reasoning

We have conducted comprehensive experimental verification of the interleaved reasoning method across five different datasets and three reinforcement learning algorithms (PPO, GRPO, and REINFORCE++).

(i) Experimental Setup

1. Datasets

We have evaluated both in-domain and out-of-domain datasets. The in-domain datasets include Knights and Knaves (K&K) and Musique, which contain subproblems and their ground truth. The out-of-domain datasets are GPQA, MMLU, and MATH, used to test the model’s generalization ability to unseen tasks and domains.

2. Models and Baselines

Experiments were conducted using Qwen2.5 instruct models with 1.5B and 7B parameters. We compared our approach with various baselines, including Direct Inference, Chain-of-Thought (CoT), Supervised Fine-Tuning (SFT), and the standard think-answer RL method proposed by Guo et al.

3. Evaluation Metrics

The primary metrics used were Pass@1 accuracy and time-to-first-token (TTFT). Pass@1 measures the proportion of problems the model solves correctly on its first attempt, while TTFT assesses how quickly the model provides useful responses to users.

(ii) Main Results

1. The Advantages of the Basic Interleaving Method

Even without intermediate rewards, the basic interleaving method (Interleave) maintains Pass@1 accuracy comparable to the traditional think-answer baseline while drastically reducing TTFT by an average of over 80%, significantly improving the model’s response speed.

2. Improvements with Intermediate Rewards

When intermediate rewards were introduced (Interleave + IR), the model’s Pass@1 accuracy improved by an average of 19.3% (for the 1.5B model) and 5.7% (for the 7B model), with TTFT further reduced by 80.7% (for the 1.5B model) and 82.2% (for the 7B model). This demonstrates that intermediate rewards can effectively enhance the model’s reasoning capability.

3. Strong Generalization Ability

Trained solely on datasets with intermediate ground truths, our method exhibits robust out-of-domain generalization across various complex reasoning tasks (GPQA, MMLU, and MATH) without requiring any training data from these domains.

V. In-Depth Analysis: Factors Influencing Interleaved Reasoning

(i) The Role of Intermediate Answers

1. Impact on Model Performance

Applying intermediate rewards during training significantly increases the number of correct intermediate answers, indicating that the reward signal effectively encourages the model to produce more accurate sub-answers, guiding it toward more reliable reasoning paths.

2. Issues with Delayed Intermediate Answers

Compared to interleaved reasoning, which provides timely feedback, delayed intermediate answer methods (where intermediate conclusions are only presented after the full reasoning trace, similar to “think-answer”) show a significant drop in Pass@1 accuracy and an increase in TTFT across multiple datasets. Additionally, the benefits of intermediate rewards are diminished in such delayed settings. This highlights the importance of timely feedback throughout the reasoning process.

(ii) Performance on Different Difficulty Levels

Using the K&K dataset as an example, as problem difficulty increases (with more characters involved), the performance gap between our method and the think-answer baseline widens. This suggests that interleaved reasoning is particularly advantageous for more complex multi-hop problems, as it helps maintain logical coherence in reasoning, making correct final conclusions more likely.

(iii) Comparison of Different Reinforcement Learning Algorithms

Among the three reinforcement learning algorithms, PPO consistently achieves higher Pass@1 scores across most tasks but generally requires more training steps to converge. In contrast, GRPO and REINFORCE++ demonstrate better sample efficiency, reaching competitive performance more rapidly, though they are less stable during training. However, regardless of the algorithm used, our interleaved reasoning method (Interleave + IR) consistently outperforms the “think + answer” baseline.

(iv) Comparison of Different Reward Strategies

Direct application of intermediate rewards (Direct IR) leads to lower accuracy, likely due to the inherent credit assignment challenges in reinforcement learning. Conditional reward strategies effectively mitigate this issue. Among them, the Time-discounted method performs the best, indicating that providing higher incentives for early correct reasoning steps can effectively guide the model toward accurate reasoning paths.

VI. Conclusion and Outlook

Interleaved reasoning, as an emerging reinforcement learning paradigm, enables reasoning large language models to generate structured intermediate answers during the thinking process. Through experiments across five different datasets and three reinforcement learning algorithms, we have confirmed that this method offers significant advantages in reducing time-to-first-token and improving Pass@1 accuracy. Moreover, the model can be trained solely on logical reasoning and question-answering datasets and demonstrates strong generalization能力 to unseen complex tasks.

The emergence of this technology paves the way for building more intelligent and interactive large language models. In the future, with further research and optimization of interleaved reasoning technology, we anticipate that large language models will play an even more significant role across various fields, bringing greater convenience to people’s lives and work.

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