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English(EN) How did Anthropic get Opus 5 so wrong from great predecessors like 4.8? Serious question here

Anthropic 的 Opus 5 因不可靠性面临用户批评

作者 PulseAugur 编辑部 · [1 个来源] ·

Reddit 上的用户对 Anthropic 的 Opus 5 模型表示严重不满，指出其在冗长和不可靠方面存在问题。他们质疑这些问题是源于新的预训练权重、有缺陷的后训练过程，还是 Opus 5 使用的特定工具。持续的批评表明，这些问题可能已深深植根于模型的架构中，因为 Anthropic 似乎几周来一直在努力修复这些问题。 AI

影响 用户反馈突显了模型开发和部署中潜在的问题，影响了信任和采用。

排序理由 用户对特定模型发布的评论。

在 r/Anthropic 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Anthropic 的 Opus 5 因不可靠性面临用户批评

本文如何被排名

Signal score
1 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
用户对特定模型发布的评论。
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, opinion
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

  1. r/Anthropic TIER_1 English(EN) · /u/py-net ·

    Anthropic的Opus 5是如何从4.8等优秀的前辈那里出错的？一个严肃的问题

    <!-- SC_OFF --><div class="md"><p>Was Opus 5 a new pre-trained weight set different from predecessors?</p> <p>Is it the same pre-training weights but post training went wrong?</p> <p>Is it in the specific harness of Opus 5?</p> <p>The verbosity and unreliability of Opus 5 has bee…

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