Viewing enriched Twitter post
New Anthropic research: We elicit capabilities from pretrained models using no external supervision, often competitive or better than using human supervision. Using this approach, we are able to train a Claude 3.5-based assistant that beats its human-supervised counterpart. https://t.co/p0wKBtRo7q
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"text": "iirc recent ``unsupervised learning'' papers focus on reasoning benchmarks + RL, while we focus more on general, fuzzy reward modeling tasks + SFT. The training dynamics of SFT feel clearer to me, e.g. SFT with random demonstrations would definitely lead to much worse performance.\nalso it seems that their results are moderately lower than training with golden rewards?\n\nI'd also be curious to see the results of INTUITOR (as well as other entropy minimization methods) on reward modeling tasks!",
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"full_text": "@xiangyuqi_pton iirc recent ``unsupervised learning'' papers focus on reasoning benchmarks + RL, while we focus more on general, fuzzy reward modeling tasks + SFT. The training dynamics of SFT feel clearer to me, e.g. SFT with random demonstrations would definitely lead to much worse…",
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"full_text": "@jiaxinwen22 @jiaxinwen22 @janleike You may find our ICLR 2025 paper (https://t.co/N0oHxvRYjG) highly relevant and to be discussed. We develop the framework that employs the joint probability of answers to a set of questions to perform fully unsupervised adaptation of an LLM/VLM.",
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