About The Workshop
The ScalR workshop focuses on the emerging challenges and opportunities in scaling and reasoning capabilities of large language models at test time. As models continue to grow in size and capability, understanding how to effectively scale their performance and enhance their reasoning abilities during inference has become increasingly important.
Call For Papers
Submissions and Review Process
We will solicit non-archival, short papers (up to 4 pages excluding references and appendix) describing novel ideas, preliminary results, or negative results related to test-time scaling for LLM reasoning.
Important Dates
- Submission Deadline: June 23, 2025
- Accept/Reject Notification: July 24, 2025
Topics of Interest
Topics of interest include, but are not limited to:
- Novel test-time algorithms for reasoning, planning, alignment, or agentic tasks
- Test-time scaling techniques for LLM agents
- Innovations in model training (algorithms, data, architecture) that facilitate more efficient and robust test-time scaling
- Test-time scaling for reasoning tasks with verifiable and non-verifiable rewards
- Novel techniques for training and utilization of outcome and process reward models in test-time scaling scenarios
- Evaluation: benchmarks, simulation environments, evaluation protocols and metrics, and human in the loop evaluation of test-time scaling methods
- Theoretical foundations of test-time scaling
- Test-time scaling techniques for multi-modal reasoning
- Studies on the faithfulness, trustworthiness, and other safety aspects of large reasoning models
- All about LLM test time scaling applications: healthcare, robotics, embodiment, chemistry, education, databases with extra encouragements to less-studied domains and beyond
- Societal implications of LLM test time scaling: bias, equity, misuse, jobs, climate change, and beyond
- Test time scaling for everyone: multi-linguality, multiculturalism, and inference time adaptations to new values