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Scalable Chain of Thoughts via Elastic Reasoning

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Manage episode 483179306 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this week's episode, we talk about Elastic Reasoning, a novel framework designed to enhance the efficiency and scalability of large reasoning models by explicitly separating the reasoning process into two distinct phases: thinking and solution.

This separation allows for independent allocation of computational budgets, addressing challenges related to uncontrolled output lengths in real-world deployments with strict resource constraints.

Our discussion explores how Elastic Reasoning contributes to more concise and efficient reasoning, even in unconstrained settings, and its implications for deploying LRMs in resource-limited environments.
Read the paper here: https://arxiv.org/pdf/2505.05315

Sign up for the next discussion & see more AI research: arize.com/ai-research-papers

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

  continue reading

49 episodes

Artwork
iconShare
 
Manage episode 483179306 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this week's episode, we talk about Elastic Reasoning, a novel framework designed to enhance the efficiency and scalability of large reasoning models by explicitly separating the reasoning process into two distinct phases: thinking and solution.

This separation allows for independent allocation of computational budgets, addressing challenges related to uncontrolled output lengths in real-world deployments with strict resource constraints.

Our discussion explores how Elastic Reasoning contributes to more concise and efficient reasoning, even in unconstrained settings, and its implications for deploying LRMs in resource-limited environments.
Read the paper here: https://arxiv.org/pdf/2505.05315

Sign up for the next discussion & see more AI research: arize.com/ai-research-papers

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

  continue reading

49 episodes

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