Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning | ScienceToStartup | ScienceToStartup

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References (28)

[1]

RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments

2025Zhiyuan Zeng, Hamish Ivison et al.

[2]

BroRL: Scaling Reinforcement Learning via Broadened Exploration

2025Jian Hu, Mingjie Liu et al.

[3]

Prompt Curriculum Learning for Efficient LLM Post-Training

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"Failure-prefix conditioning enhances RLVR by focusing on rare incorrect reasoning trajectories to extend training on saturated problems."

LLM Training•Score: 3•View PDF ↗

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Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning

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References (28)

Founder's Pitch

"Failure-prefix conditioning enhances RLVR by focusing on rare incorrect reasoning trajectories to extend training on saturated problems."

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Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning

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References (28)

Founder's Pitch

"Failure-prefix conditioning enhances RLVR by focusing on rare incorrect reasoning trajectories to extend training on saturated problems."

Commercial Viability Breakdown

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