Reasoning with Context Management and External Memory

Key points

• Reasoning models maintain information about the status of their reasoning process through in-context memory.1
• Complex problems require larger context windows and memory requirements.1
• In this scenario, context management without caching risks discarding useful information for reasoning.1
• We demonstrate our approach on a few mathematics problems using model sizes ranging from 0.4 to 25 million parameters, and GPT-2.1

Sources (1)

• [1]Reasoning with Context Management and External Memory
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 08:05 PM

  We propose RCMEM, an approach that extends context management with an external memory to maintain additional information without burdening the reasoning model with additional context.

  Reasoning models maintain information about the status of their reasoning process through in-context memory.

Extractive summary: sentences quoted from the sources.