Recent large language models (LLMs) have demonstrated strong reasoning capabilities that benefit from online reinforcement learning (RL), particularly within the left-to-right autoregressive (AR) generation paradigm. However, non-autoregressive paradigms based on diffusion generate text in a coarse-to-fine manner, presenting an alternative approach. Recent diffusion-based large language models (dLLMs) have shown competitive language modeling performance compared to their AR counterparts but it is unclear if they can leverage recent advances in LLM reasoning. In response to this gap, the d1 framework has been proposed to adapt pre-trained masked dLLMs into reasoning models through a combination of supervised fine-tuning (SFT) and RL. This framework introduces techniques aimed at enhancing reasoning in pretrained dLLMs, including a masked SFT technique to distill knowledge and instill self-improvement behavior from existing datasets, as well as a novel critic-free policy-gradient based RL algorithm called diffu-GRPO. Empirical studies have been conducted to evaluate the performance of different post-training recipes on various mathematical and logical reasoning benchmarks. The results indicate that d1 yields the best performance and significantly enhances the performance of state-of-the-art dLLMs. Additionally, advancements in scaling diffusion language models have been explored, with masked diffusion established as a specific instance of discrete diffusion. Efforts are being made to address challenges related to scalability and discretization in order to further improve reasoning abilities in large diffusion language models through reinforcement learning strategies.
- Recent large language models (LLMs) have strong reasoning capabilities
- Online reinforcement learning (RL) benefits LLMs, especially in left-to-right autoregressive (AR) generation
- Diffusion-based large language models (dLLMs) generate text in a coarse-to-fine manner
- dLLMs show competitive performance compared to AR models but it's unclear if they can leverage recent advances in reasoning
- The d1 framework adapts pre-trained masked dLLMs into reasoning models through supervised fine-tuning and RL
- Techniques like masked SFT and diffu-GRPO RL algorithm are used to enhance reasoning in pretrained dLLMs
- Empirical studies show that d1 framework yields the best performance and enhances state-of-the-art dLLMs
- Advancements in scaling diffusion language models include masked diffusion as a specific instance of discrete diffusion
- Efforts are being made to address challenges related to scalability and discretization for further improvement in reasoning abilities
Summary1. Big computer programs can think well.
2. Learning online helps these programs, especially when they write from left to right.
3. Some programs write step by step, starting rough and getting better.
4. One type of program competes well with others but may not be as good at thinking.
5. A new way makes these programs smarter through practice and rules.
Definitions- Language models: Computer programs that understand and generate human language.
- Reinforcement learning: A type of machine learning where a program learns by trial and error, receiving rewards for good actions.
- Autoregressive generation: Writing or generating text one word at a time in order.
- Diffusion-based models: Programs that generate text gradually improving its quality.
- Supervised fine-tuning: Adjusting a pre-trained model to perform better on specific tasks with guidance data.
- Empirical studies: Experiments based on observations and real-world data to draw conclusions.
Recent advancements in large language models (LLMs) have shown impressive reasoning capabilities, particularly when combined with online reinforcement learning (RL). However, recent research has also explored the potential of non-autoregressive paradigms based on diffusion for generating text in a coarse-to-fine manner. This presents an alternative approach to traditional left-to-right autoregressive (AR) generation methods.
In response to this gap, a new framework called d1 has been proposed to adapt pre-trained masked diffusion-based large language models (dLLMs) into reasoning models through a combination of supervised fine-tuning (SFT) and RL. This framework introduces techniques aimed at enhancing reasoning in pretrained dLLMs, including a masked SFT technique and a novel critic-free policy-gradient based RL algorithm called diffu-GRPO.
The main goal of the d1 framework is to improve the performance of state-of-the-art dLLMs on various mathematical and logical reasoning benchmarks. To achieve this, it utilizes a combination of SFT and RL strategies to enhance the reasoning abilities of these models. The results from empirical studies show that d1 yields the best performance compared to other post-training recipes and significantly improves upon the performance of existing state-of-the-art dLLMs.
One key aspect of the d1 framework is its use of masked SFT technique which aims to distill knowledge from existing datasets and instill self-improvement behavior in pretrained dLLMs. This allows these models to learn from their mistakes and continuously improve their reasoning abilities over time. Additionally, diffu-GRPO provides a novel way for these models to learn through reinforcement learning without relying on external critics or evaluators.
Another important area that has been explored in recent research is scaling diffusion language models. Masked diffusion has been established as one specific instance of discrete diffusion which shows promising results for improving reasoning capabilities in large LLMs through reinforcement learning strategies. However, there are still challenges related to scalability and discretization that need to be addressed in order to fully leverage the potential of diffusion-based LLMs for reasoning tasks.
In conclusion, recent research has shown that non-autoregressive paradigms based on diffusion have the potential to improve reasoning capabilities in large language models. The d1 framework, with its combination of SFT and RL techniques, has demonstrated significant improvements in performance compared to existing state-of-the-art dLLMs. Further advancements in scaling diffusion language models and addressing challenges related to scalability and discretization will continue to enhance the reasoning abilities of these models through reinforcement learning strategies. This opens up new possibilities for using large LLMs for complex reasoning tasks, bringing us closer to achieving truly intelligent natural language processing systems.
