# Parallel reasoning

Published articles for Parallel reasoning.

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## Diverse reasoning traces teach LLMs to make better decisions

DevFeed: [Diverse reasoning traces teach LLMs to make better decisions](<https://devfeed.tech/articles/diverse-reasoning-traces-teach-llms-to-make-better-decisions-7597.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/diverse-reasoning-traces-teach-llms-to-make-better-decisions>)

Author: Sheng Jia; Xiao Wang; Shiva Kasiviswanathan

Published: 2026-05-26T15:17:06Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [code-generation](<https://devfeed.tech/tags/code-generation.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [iclr](<https://devfeed.tech/tags/iclr.md>), [iclr-2026](<https://devfeed.tech/tags/iclr-2026.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llms](<https://devfeed.tech/tags/llms.md>), [math-reasoning](<https://devfeed.tech/tags/math-reasoning.md>), [parallel-reasoning](<https://devfeed.tech/tags/parallel-reasoning.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [post-training-optimization](<https://devfeed.tech/tags/post-training-optimization.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [traces](<https://devfeed.tech/tags/traces.md>)

### AI overview

The article presents set-supervised fine tuning and global forking policy optimization to train LLMs on multiple distinct reasoning paths. It reports 5% to 7% single-shot accuracy gains on standard benchmarks.

### Source excerpt

How to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies.