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محتوای ارائه شده توسط LessWrong. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط LessWrong یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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“Towards a Typology of Strange LLM Chains-of-Thought” by 1a3orn

17:34
 
اشتراک گذاری
 

Manage episode 512930004 series 3364758
محتوای ارائه شده توسط LessWrong. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط LessWrong یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Intro
LLMs being trained with RLVR (Reinforcement Learning from Verifiable Rewards) start off with a 'chain-of-thought' (CoT) in whatever language the LLM was originally trained on. But after a long period of training, the CoT sometimes starts to look very weird; to resemble no human language; or even to grow completely unintelligible.
Why might this happen?
I've seen a lot of speculation about why. But a lot of this speculation narrows too quickly, to just one or two hypotheses. My intent is also to speculate, but more broadly.
Specifically, I want to outline six nonexclusive possible causes for the weird tokens: new better language, spandrels, context refresh, deliberate obfuscation, natural drift, and conflicting shards.
And I also wish to extremely roughly outline ideas for experiments and evidence that could help us distinguish these causes.
I'm sure I'm not enumerating the full space of [...]
---
Outline:
(00:11) Intro
(01:34) 1. New Better Language
(04:06) 2. Spandrels
(06:42) 3. Context Refresh
(10:48) 4. Deliberate Obfuscation
(12:36) 5. Natural Drift
(13:42) 6. Conflicting Shards
(15:24) Conclusion
---
First published:
October 9th, 2025
Source:
https://www.lesswrong.com/posts/qgvSMwRrdqoDMJJnD/towards-a-typology-of-strange-llm-chains-of-thought
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Table comparing unusual word frequencies between OpenAI o3 and GPQA baseline.
Quadrant chart titled Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
  continue reading

650 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 512930004 series 3364758
محتوای ارائه شده توسط LessWrong. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط LessWrong یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Intro
LLMs being trained with RLVR (Reinforcement Learning from Verifiable Rewards) start off with a 'chain-of-thought' (CoT) in whatever language the LLM was originally trained on. But after a long period of training, the CoT sometimes starts to look very weird; to resemble no human language; or even to grow completely unintelligible.
Why might this happen?
I've seen a lot of speculation about why. But a lot of this speculation narrows too quickly, to just one or two hypotheses. My intent is also to speculate, but more broadly.
Specifically, I want to outline six nonexclusive possible causes for the weird tokens: new better language, spandrels, context refresh, deliberate obfuscation, natural drift, and conflicting shards.
And I also wish to extremely roughly outline ideas for experiments and evidence that could help us distinguish these causes.
I'm sure I'm not enumerating the full space of [...]
---
Outline:
(00:11) Intro
(01:34) 1. New Better Language
(04:06) 2. Spandrels
(06:42) 3. Context Refresh
(10:48) 4. Deliberate Obfuscation
(12:36) 5. Natural Drift
(13:42) 6. Conflicting Shards
(15:24) Conclusion
---
First published:
October 9th, 2025
Source:
https://www.lesswrong.com/posts/qgvSMwRrdqoDMJJnD/towards-a-typology-of-strange-llm-chains-of-thought
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Table comparing unusual word frequencies between OpenAI o3 and GPQA baseline.
Quadrant chart titled Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
  continue reading

650 قسمت

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