Librarian Bot. I found the following papers similar to this paper.

\n

The following papers were recommended by the Semantic Scholar API

\n

\n
• Compute-Accuracy Pareto Frontiers for Open-Source Reasoning Large Language Models (2025)
\n
• Quantifying the Effect of Test Set Contamination on Generative Evaluations (2026)
\n
• Neural Neural Scaling Laws (2026)
\n
• Data Repetition Beats Data Scaling in Long-CoT Supervised Fine-Tuning (2026)
\n
• Unveiling Scaling Behaviors in Molecular Language Models: Effects of Model Size, Data, and Representation (2026)
\n
• Theoretical Foundations of Scaling Law in Familial Models (2025)
\n
• Weight Decay Improves Language Model Plasticity (2026)
\n

\n

Please give a thumbs up to this comment if you found it helpful!

\n

If you want recommendations for any Paper on Hugging Face checkout this Space

\n

You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: \n\n@librarian-bot\n\t recommend

\n","updatedAt":"2026-02-19T01:40:28.779Z","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":317,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7204253077507019},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2602.15327","authors":[{"_id":"699550588d17d1ee8c10ecd7","user":{"_id":"624054bcc2c17da6a63eb539","avatarUrl":"/avatars/bf52dc0683b4100733f8696a97696d0e.svg","isPro":true,"fullname":"hlzhang109","user":"hlzhang109","type":"user"},"name":"Hanlin Zhang","status":"admin_assigned","statusLastChangedAt":"2026-02-18T13:45:20.828Z","hidden":false},{"_id":"699550588d17d1ee8c10ecd8","name":"Jikai Jin","hidden":false},{"_id":"699550588d17d1ee8c10ecd9","user":{"_id":"690526084ae4c0fc109fbaef","avatarUrl":"/avatars/87e1257024d79c4469dad48a8bb3bdab.svg","isPro":false,"fullname":"Vasilis Syrgkanis","user":"vsyrgk","type":"user"},"name":"Vasilis Syrgkanis","status":"admin_assigned","statusLastChangedAt":"2026-02-18T13:45:26.632Z","hidden":false},{"_id":"699550588d17d1ee8c10ecda","name":"Sham Kakade","hidden":false}],"publishedAt":"2026-02-17T03:13:51.000Z","submittedOnDailyAt":"2026-02-18T03:13:27.451Z","title":"Prescriptive Scaling Reveals the Evolution of Language Model Capabilities","submittedOnDailyBy":{"_id":"624054bcc2c17da6a63eb539","avatarUrl":"/avatars/bf52dc0683b4100733f8696a97696d0e.svg","isPro":true,"fullname":"hlzhang109","user":"hlzhang109","type":"user"},"summary":"For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is attainable with contemporary post training practice, and how stable is that mapping as the field evolves? Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability boundaries, high conditional quantiles of benchmark scores as a function of log pre training FLOPs, via smoothed quantile regression with a monotone, saturating sigmoid parameterization. We validate the temporal reliability by fitting on earlier model generations and evaluating on later releases. Across various tasks, the estimated boundaries are mostly stable, with the exception of math reasoning that exhibits a consistently advancing boundary over time. We then extend our approach to analyze task dependent saturation and to probe contamination related shifts on math reasoning tasks. Finally, we introduce an efficient algorithm that recovers near full data frontiers using roughly 20% of evaluation budget. Together, our work releases the Proteus 2k, the latest model performance evaluation dataset, and introduces a practical methodology for translating compute budgets into reliable performance expectations and for monitoring when capability boundaries shift across time.","upvotes":2,"discussionId":"699550588d17d1ee8c10ecdb","projectPage":"https://jkjin.com/prescriptive-scaling","ai_summary":"Large-scale observational analysis estimates capability boundaries and performance predictions for foundation models using quantile regression and evaluates temporal stability across tasks.","ai_keywords":["scaling laws","quantile regression","sigmoid parameterization","conditional quantiles","observational evaluations","model performance","compute budget","capability boundaries","temporal reliability","smoothing"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"620783f24e28382272337ba4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620783f24e28382272337ba4/zkUveQPNiDfYjgGhuFErj.jpeg","isPro":false,"fullname":"GuoLiangTang","user":"Tommy930","type":"user"},{"_id":"684d57f26e04c265777ead3f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/cuOj-bQqukSZreXgUJlfm.png","isPro":false,"fullname":"Joakim Lee","user":"Reinforcement4All","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":0}">