Redwood Research advocates differentially accelerating AI conceptual reasoning

1. 15:06
   X@alec_harris_x· Discussion

   New post: How much will conceptual reasoning training transfer to ML research abilities? Redwood's CRC team recently r…(opens the original post in a new tab)
2. 14:55
   LessWrong

   To what extent can conceptual reasoning training transfer to ML research capability?

   Regarding the controversy over the Redwood CRC team accelerating conceptual reasoning training, the authors quantitatively analyze how much of AI safety conceptual reasoning training transfers to ML research capability. There are three considerations: conceptual reasoning skills are partly domain-specific, ML research is less conceptual, and ML research is more saturated due to RLVR. They envision training Opus 4.5 only with AI safety-domain LMCA-style questions until LMCA improves by 20 points, then testing cross-domain transfer; the CRC team says it has lowered expectations for cross-domain generalization.

1. 12:24
   Redwood Research

   The basic argument for supporting the acceleration of AI conceptual reasoning capabilities

   Emery Cooper of Redwood Research wrote an article arguing that AI's conceptual reasoning capabilities should be accelerated differentially, that is, tasks that are similar to philosophy, difficult to verify directly, and advanced mainly through argumentation. The article holds that important work such as alignment cannot do without this kind of reasoning, whereas AI R&D and takeover depend on it far less, so accelerating it helps safety work more than it helps general capabilities. The author states that default training is dominated by easily verifiable tasks and companies lack commercial incentives, so conceptual reasoning is easily overlooked; their experiments show that few-shot prompting and small-scale training can improve models by three to twelve months on their conceptual argumentation benchmark LMCA. The author expects such work to spill over little into general capabilities; at present it mainly produces conceptual reasoning datasets and benchmarks, released through the Conceptual Reasoning Index website, with priority directions including technical AI safety, decision theory and safe Pareto improvements, and is recruiting researchers to participate.

2. 00:17
   LessWrong

   The basic argument for differentially accelerating AI's conceptual reasoning capabilities

   The authors propose that AI's conceptual reasoning capabilities should be differentially accelerated: key work such as alignment depends on conceptual reasoning that is difficult to verify directly, and the AI safety community currently invests insufficiently in this direction. Because AI R&D and takeover depend far less on conceptual reasoning, such acceleration would help safety work far more than general capabilities. The authors state that basic steering methods such as few-shot prompting and small-scale training can improve models' scores on their conceptual reasoning benchmark by an amount equivalent to 3 to 12 months of frontier progress.

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