Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456 Paper page - Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
https://ssundaram21.github.io/soar/ !\n","updatedAt":"2026-01-27T09:49:08.119Z","author":{"_id":"65ce30e06da01df536eded5a","avatarUrl":"/avatars/04c32cba7a3bbaf9ea5dee88c96cf87b.svg","fullname":"Julia Kempe","name":"Knykny","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.9094945788383484},"editors":["Knykny"],"editorAvatarUrls":["/avatars/04c32cba7a3bbaf9ea5dee88c96cf87b.svg"],"reactions":[],"isReport":false}},{"id":"6979689130aff170dd115070","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false},"createdAt":"2026-01-28T01:38:25.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Guided Self-Evolving LLMs with Minimal Human Supervision](https://huggingface.co/papers/2512.02472) (2025)\n* [DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution](https://huggingface.co/papers/2601.13761) (2026)\n* [Semantic Soft Bootstrapping: Long Context Reasoning in LLMs without Reinforcement Learning](https://huggingface.co/papers/2512.05105) (2025)\n* [Dr. Zero: Self-Evolving Search Agents without Training Data](https://huggingface.co/papers/2601.07055) (2026)\n* [Teaching Large Reasoning Models Effective Reflection](https://huggingface.co/papers/2601.12720) (2026)\n* [CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning](https://huggingface.co/papers/2512.18857) (2025)\n* [SPARK: Stepwise Process-Aware Rewards for Reference-Free Reinforcement Learning](https://huggingface.co/papers/2512.03244) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"
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Reinforcement learning methods for finetuning large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigate a fundamental question: Can a pretrained LLM leverage latent knowledge to generate an automated curriculum for problems it cannot solve? To explore this, we design SOAR: A self-improvement framework designed to surface these pedagogical signals through meta-RL. A teacher copy of the model proposes synthetic problems for a student copy, and is rewarded with its improvement on a small subset of hard problems. Critically, SOAR grounds the curriculum in measured student progress rather than intrinsic proxy rewards. Our study on the hardest subsets of mathematical benchmarks (0/128 success) reveals three core findings. First, we show that it is possible to realize bi-level meta-RL that unlocks learning under sparse, binary rewards by sharpening a latent capacity of pretrained models to generate useful stepping stones. Second, grounded rewards outperform intrinsic reward schemes used in prior LLM self-play, reliably avoiding the instability and diversity collapse modes they typically exhibit. Third, analyzing the generated questions reveals that structural quality and well-posedness are more critical for learning progress than solution correctness. Our results suggest that the ability to generate useful stepping stones does not require the preexisting ability to actually solve the hard problems, paving a principled path to escape reasoning plateaus without additional curated data.","upvotes":40,"discussionId":"69783109026bdf0473116e42","ai_summary":"A self-improvement framework enables pretrained language models to generate automated curricula for solving previously unsolvable problems by leveraging latent knowledge and meta-reinforcement learning.","ai_keywords":["pretrained LLM","reinforcement learning","finetuning","meta-RL","automated curriculum","self-improvement framework","teacher-student model","binary rewards","sparse rewards","latent capacity","stepping stones","structural quality","well-posedness"],"organization":{"_id":"5e63d8713071d5be688861b8","name":"facebook","fullname":"AI at Meta","avatar":"https://cdn-uploads.huggingface.co/production/uploads/1592839207516-noauth.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"65ce30e06da01df536eded5a","avatarUrl":"/avatars/04c32cba7a3bbaf9ea5dee88c96cf87b.svg","isPro":false,"fullname":"Julia Kempe","user":"Knykny","type":"user"},{"_id":"69785ccead94585f418e706c","avatarUrl":"/avatars/7f8e02cb71b79eee4413e7439dbabc05.svg","isPro":false,"fullname":"zhang","user":"zhangml233","type":"user"},{"_id":"697862fd67aaca70b2c2daaa","avatarUrl":"/avatars/ecb3a79160a6623ef04907c69d7efa18.svg","isPro":false,"fullname":"yizhang","user":"ModelWeaver","type":"user"},{"_id":"680a2f6202f8f5eddd0f0873","avatarUrl":"/avatars/d71093b223d532599387287a20d15c52.svg","isPro":false,"fullname":"N","user":"Gaetan10","type":"user"},{"_id":"6328ab511558dac67c45af92","avatarUrl":"/avatars/1134657afe749b782f89fcabe960b774.svg","isPro":false,"fullname":"Shobhita Sundaram","user":"ssundaram","type":"user"},{"_id":"662938fe85faa365a7a59645","avatarUrl":"/avatars/0958564dffb8b2fd15da09623587d462.svg","isPro":false,"fullname":"Charles Arnal","user":"CharlesArnal","type":"user"},{"_id":"625de0717341c641426e7932","avatarUrl":"/avatars/9deb06fc565a80002c3ae75c6f4cd9e7.svg","isPro":false,"fullname":"Ariel Kwiatkowski","user":"RedTachyon","type":"user"},{"_id":"67f3d73aef6bf6f714f30c30","avatarUrl":"/avatars/ae78cc0d1e2c11b3c64b3f379e3e6c03.svg","isPro":false,"fullname":"Ismail Labiad","user":"ilabiad","type":"user"},{"_id":"697888efee98948fbb10c17b","avatarUrl":"/avatars/153854597d89b6a8aa633cbad97f5aab.svg","isPro":false,"fullname":"Kartik Ahuja","user":"ahujak","type":"user"},{"_id":"697892e3737b26a1852a3a19","avatarUrl":"/avatars/1b199c802a1d0b71171202e380b9dc54.svg","isPro":false,"fullname":"Zhao Yun","user":"ZhaoYunAI","type":"user"},{"_id":"63c1699e40a26dd2db32400d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63c1699e40a26dd2db32400d/3N0-Zp8igv8-52mXAdiiq.jpeg","isPro":false,"fullname":"Chroma","user":"Chroma111","type":"user"},{"_id":"67a79d33a4e7c29abb4bbf3c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/qc3DLYZiGQgD9SxUVzavd.jpeg","isPro":false,"fullname":"Marius Dinca","user":"Puddings22","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":0,"organization":{"_id":"5e63d8713071d5be688861b8","name":"facebook","fullname":"AI at Meta","avatar":"https://cdn-uploads.huggingface.co/production/uploads/1592839207516-noauth.png"}}">
A self-improvement framework enables pretrained language models to generate automated curricula for solving previously unsolvable problems by leveraging latent knowledge and meta-reinforcement learning.
AI-generated summary
Can a model learn to escape its own learning plateau? Reinforcement learning methods for finetuning large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigate a fundamental question: Can a pretrained LLM leverage latent knowledge to generate an automated curriculum for problems it cannot solve? To explore this, we design SOAR: A self-improvement framework designed to surface these pedagogical signals through meta-RL. A teacher copy of the model proposes synthetic problems for a student copy, and is rewarded with its improvement on a small subset of hard problems. Critically, SOAR grounds the curriculum in measured student progress rather than intrinsic proxy rewards. Our study on the hardest subsets of mathematical benchmarks (0/128 success) reveals three core findings. First, we show that it is possible to realize bi-level meta-RL that unlocks learning under sparse, binary rewards by sharpening a latent capacity of pretrained models to generate useful stepping stones. Second, grounded rewards outperform intrinsic reward schemes used in prior LLM self-play, reliably avoiding the instability and diversity collapse modes they typically exhibit. Third, analyzing the generated questions reveals that structural quality and well-posedness are more critical for learning progress than solution correctness. Our results suggest that the ability to generate useful stepping stones does not require the preexisting ability to actually solve the hard problems, paving a principled path to escape reasoning plateaus without additional curated data.