Unverified50% confidenceFactExact time
JOLT is not the first work to attempt to compress post-training costs; previous directions include rejection sampling fine-tuning, the direct preference optimization (DPO) family of methods, and various self-rewarding mechanisms.
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10/24/2026
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VerifiedAlignment algorithms such as DPO (Direct Preference Optimization) reduce training costs while improving the consistency of model performance on open-ended instructions.77% similarUnverified训练SWE-bench智能体的先进方法包括拒绝采样（Rejection Sampling）、DPO（Direct Preference Optimization）以及过程奖励模型（Process Reward Model）74% similarUnverifiedSatisDive adopts a training-free strategy that takes effect at inference time, introducing batch-relative reward truncation to distinguish between low-reward and high-reward candidates.69% similarUnverifiedThe true cost reductions come from post-training optimization, request routing, and capacity terms negotiated down based on usage volume; the focus should be on the cost per completed task, not the token unit price.68% similar
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