# Workshop on Reasoning and Planning for Large Language Models

## About The Workshop
This workshop explores the growing capabilities of large language models (LLMs), such as OpenAI's o1 model, in reasoning, planning, and decision-making, highlighting recent advances and challenges. We aim to examine how reinforcement learning methods, post-training optimization, and efficient inference techniques can further enhance LLMs' reasoning capabilities. Topics include training approach for enhancing reasoning and planning abilities, scaling inference for complex tasks, developing robust benchmarks, and extending LLMs to multi-modal and embodied environments. We will also discuss broader themes such as causal reasoning, collaborative multi-agent systems, uncertainty, and explainability to offer insights and guidance for the further development of reasoning and planning in LLMs.

## Topics
The workshop will cover a range of topics, including but not limited to:

1. Training Methodologies for Enhancing Reasoning and Planning Capabilities in LLMs:
We will explore the application of RL algorithms and other effective approaches in enhancing LLM reasoning and planning abilities during both pre-training and post-training stages. We will examine how techniques like Reinforcement Learning from Human Feedback (RLHF) can be adapted and expanded for efficient reasoning. Key questions include:

- How can RL and other effective methods be utilized in pre-training to improve reasoning abilities?
- What post-training approaches (e.g., fine-tuning, RLHF) are most effective for LLM planning tasks?
- How can synthetic data generation and self-supervised training enhance LLM reasoning and planning?


2. Inference Time Scaling for Complex Reasoning Tasks:
We will discuss challenges and innovations in scaling up reasoning during inference. As models become larger and tasks more complex, efficient inference mechanisms are critical. Topics of interest include:

- What are the most promising methods for scaling inference times in reasoning-heavy tasks?
- How can models dynamically allocate resources during inference to optimize for reasoning and planning?

3. Benchmarking Reasoning and Planning:
Developing robust benchmarks for evaluating reasoning and planning in LLMs is critical to track progress. This session will address the need for new metrics and standardized tasks to assess reasoning abilities across different scenarios. Key discussions will include:

- What benchmarks can accurately reflect the reasoning and planning capabilities of LLMs?
- How do we design tasks that evaluate long-horizon reasoning and complex decision-making?

4. Multi-modality and Embodiment in LLMs:
As LLMs increasingly integrate with multi-modal environments, reasoning across multiple data types (e.g., vision, sound, text) becomes more essential. This session will explore the application of reasoning and planning in multi-modality and embodied AI systems, including robotics and real-world interactions:

- How can LLMs enhance multi-modal reasoning and planning to better interact with diverse environments?
- What are the key challenges and opportunities in applying LLMs to multi-modal tasks, including those requiring embodied reasoning?

5. Exploring Broader Topics in Reasoning and Planning:
In addition to the core themes mentioned above, our discussions will also encompass a broader range of emerging topics, including:

- Causal Reasoning: How can LLMs move beyond pattern recognition to infer causal relationships?
- Collaborative Reasoning in Multi-Agent Systems: How can LLMs enable multi-agent cooperation for distributed tasks?
- Uncertainty and Robustness: How can LLMs improve reasoning under ambiguous information?
- Human-in-the-Loop Systems: How can human feedback refine LLM decision-making processes?
- Explainability: How can we make LLM reasoning and planning more transparent and interpretable for real-world applications?


## Scope

We welcome contributions across a broad spectrum of topics, including but not limited to:
- Training methodologies for enhancing reasoning and planning in LLMs
- Efficient inference for complex reasoning tasks
- Benchmarking reasoning and planning capabilities
- Multi-modality and embodiment in LLMs
- Emerging trends in LLM reasoning and planning