Merge pull request #24 from ScalingIntelligence/tanvir_dev

Potential local to deploy fix

Author: jonsaadfalcon

_pubs/monkeyspower.md

@@ -28,14 +28,11 @@ has_pdf: true
 doi: 10.48550/arXiv.2502.17578
 tags:
   - machine learning
-  - scaling laws
-  - generative AI
-  - inference compute
-teaser: 
-  Recent research documents a surprising finding: increasing inference compute through repeated sampling reveals power-law scaling in average success rates. This occurs despite per-problem exponential scaling, explained by heavy-tailed distributions of single-attempt success rates. Our analysis unifies these observations under a mathematical framework, offering new insights into inference-time scaling laws and more efficient performance forecasting for language models.
+  - generative ai
+teaser: Recent research documents a surprising finding: increasing inference compute through repeated sampling reveals power-law scaling in average success rates. This occurs despite per-problem exponential scaling, explained by heavy-tailed distributions of single-attempt success rates. Our analysis unifies these observations under a mathematical framework, offering new insights into inference-time scaling laws and more efficient performance forecasting for language models.
 materials:
   - name: Paper
     url: https://arxiv.org/abs/2502.17578
     type: file-pdf
 ---
-Recent research across mathematical problem solving, proof assistant programming and multimodal jailbreaking documents a striking finding: when (multimodal) language model tackle a suite of tasks with multiple attempts per task -- succeeding if any attempt is correct -- then the negative log of the average success rate scales a power law in the number of attempts. In this work, we identify an apparent puzzle: a simple mathematical calculation predicts that on each problem, the failure rate should fall exponentially with the number of attempts. We confirm this prediction empirically, raising a question: from where does aggregate polynomial scaling emerge? We then answer this question by demonstrating per-problem exponential scaling can be made consistent with aggregate polynomial scaling if the distribution of single-attempt success probabilities is heavy tailed such that a small fraction of tasks with extremely low success probabilities collectively warp the aggregate success trend into a power law - even as each problem scales exponentially on its own. We further demonstrate that this distributional perspective explains previously observed deviations from power law scaling, and provides a simple method for forecasting the power law exponent with an order of magnitude lower relative error, or equivalently, ~2-4 orders of magnitude less inference compute. Overall, our work contributes to a better understanding of how neural language model performance improves with scaling inference compute and the development of scaling-predictable evaluations of (multimodal) language models.
+Recent research across mathematical problem solving, proof assistant programming and multimodal jailbreaking documents a striking finding: when (multimodal) language model tackle a suite of tasks with multiple attempts per task -- succeeding if any attempt is correct -- then the negative log of the average success rate scales a power law in the number of attempts. In this work, we identify an apparent puzzle: a simple mathematical calculation predicts that on each problem, the failure rate should fall exponentially with the number of attempts. We confirm this prediction empirically, raising a question: from where does aggregate polynomial scaling emerge? We then answer this question by demonstrating per-problem exponential scaling can be made consistent with aggregate polynomial scaling if the distribution of single-attempt success probabilities is heavy tailed such that a small fraction of tasks with extremely low success probabilities collectively warp the aggregate success trend into a power law - even as each problem scales exponentially on its own. We further demonstrate that this distributional perspective explains previously observed deviations from power law scaling, and provides a simple method for forecasting the power law exponent with an order of magnitude lower relative error, or equivalently, ~2-4 orders of magnitude less inference compute. Overall, our work contributes to a better understanding of how neural language model performance improves with scaling inference compute and the development of scaling-predictable evaluations of (multimodal) language models.

_pubs/sdgrl.md

@@ -19,14 +19,9 @@ day: 28
 has_pdf: true
 doi: 10.48550/arXiv.2504.04736
 tags:
-  - reinforcement learning
-  - language models
-  - reasoning
-  - tool use
-  - synthetic data
-  - generative AI
-teaser: 
-  This paper introduces Step-Wise Reinforcement Learning (SWiRL), a framework for improving multi-step reasoning and tool use in language models through synthetic data generation and offline RL. SWiRL decomposes reasoning trajectories into sub-trajectories, enabling fine-grained feedback and significant accuracy improvements across challenging tasks like HotPotQA, GSM8K, MuSiQue, CofCA, and BeerQA. Notably, SWiRL-trained models outperform larger proprietary models in multi-step reasoning while demonstrating strong task generalization and improved cost efficiency.
+  - model
+  - generative ai
+teaser: This paper introduces Step-Wise Reinforcement Learning (SWiRL), a framework for improving multi-step reasoning and tool use in language models through synthetic data generation and offline RL. SWiRL decomposes reasoning trajectories into sub-trajectories, enabling fine-grained feedback and significant accuracy improvements across challenging tasks like HotPotQA, GSM8K, MuSiQue, CofCA, and BeerQA. Notably, SWiRL-trained models outperform larger proprietary models in multi-step reasoning while demonstrating strong task generalization and improved cost efficiency.
 materials:
   - name: Paper
     url: https://arxiv.org/abs/2504.04736

_pubs/tpt.md

@@ -14,12 +14,9 @@ has_pdf: true
 doi: 10.48550/arXiv.2504.18116
 tags:
   - machine learning
-  - language models
-  - reasoning
-  - self-improvement
-  - generative AI
-teaser: 
-  This paper introduces the Think, Prune, Train (TPT) framework, a scalable method for improving language model reasoning without increasing model size. By iteratively fine-tuning models on their own reasoning traces and applying correctness-based pruning, TPT enables smaller models to achieve performance rivaling or exceeding larger ones. Experimental results on GSM8K and CodeContests show that models like Gemma-2B and LLaMA-70B-Instruct can surpass even GPT-4o on Pass@1 accuracy through recursive self-improvement.
+  - model
+  - generative ai
+teaser: This paper introduces the Think, Prune, Train (TPT) framework, a scalable method for improving language model reasoning without increasing model size. By iteratively fine-tuning models on their own reasoning traces and applying correctness-based pruning, TPT enables smaller models to achieve performance rivaling or exceeding larger ones. Experimental results on GSM8K and CodeContests show that models like Gemma-2B and LLaMA-70B-Instruct can surpass even GPT-4o on Pass@1 accuracy through recursive self-improvement.
 materials:
   - name: Paper
     url: https://arxiv.org/abs/2504.18116