Auto. Make Doomgrad HF Review on 29 January

d/2025-01-29.json

@@ -4,9 +4,9 @@
         "en": "January 29",
         "zh": "1月29日"
     },
-    "time_utc": "2025-01-29 20:11",
+    "time_utc": "2025-01-29 21:09",
     "weekday": 2,
-    "issue_id": 1934,
+    "issue_id": 1935,
     "home_page_url": "https://huggingface.co/papers",
     "papers": [
         {
@@ -345,7 +345,7 @@
             "title": "IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding",
             "url": "https://huggingface.co/papers/2501.15747",
             "abstract": "Known by more than 1.5 billion people in the Indian subcontinent, Indic languages present unique challenges and opportunities for natural language processing (NLP) research due to their rich cultural heritage, linguistic diversity, and complex structures. IndicMMLU-Pro is a comprehensive benchmark designed to evaluate Large Language Models (LLMs) across Indic languages, building upon the MMLU Pro (Massive Multitask Language Understanding) framework. Covering major languages such as Hindi, Bengali, Gujarati, Marathi, Kannada, Punjabi, Tamil, Telugu, and Urdu, our benchmark addresses the unique challenges and opportunities presented by the linguistic diversity of the Indian subcontinent. This benchmark encompasses a wide range of tasks in language comprehension, reasoning, and generation, meticulously crafted to capture the intricacies of Indian languages. IndicMMLU-Pro provides a standardized evaluation framework to push the research boundaries in Indic language AI, facilitating the development of more accurate, efficient, and culturally sensitive models. This paper outlines the benchmarks' design principles, task taxonomy, and data collection methodology, and presents baseline results from state-of-the-art multilingual models.",
-            "score": 3,
+            "score": 4,
             "issue_id": 1918,
             "pub_date": "2025-01-27",
             "pub_date_card": {

hf_papers.json

@@ -4,17 +4,17 @@
         "en": "January 29",
         "zh": "1月29日"
     },
-    "time_utc": "2025-01-29 21:09",
+    "time_utc": "2025-01-29 22:09",
     "weekday": 2,
-    "issue_id": 1935,
+    "issue_id": 1936,
     "home_page_url": "https://huggingface.co/papers",
     "papers": [
         {
             "id": "https://huggingface.co/papers/2501.17161",
             "title": "SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training",
             "url": "https://huggingface.co/papers/2501.17161",
             "abstract": "Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models. However, their roles in enhancing model generalization capabilities remain unclear. This paper studies the difference between SFT and RL on generalization and memorization, focusing on text-based rule variants and visual variants. We introduce GeneralPoints, an arithmetic reasoning card game, and adopt V-IRL, a real-world navigation environment, to assess how models trained with SFT and RL generalize to unseen variants in both textual and visual domains. We show that RL, especially when trained with an outcome-based reward, generalizes across both rule-based textual and visual variants. SFT, in contrast, tends to memorize training data and struggles to generalize out-of-distribution scenarios. Further analysis reveals that RL improves the model's underlying visual recognition capabilities, contributing to its enhanced generalization in the visual domain. Despite RL's superior generalization, we show that SFT remains essential for effective RL training; SFT stabilizes the model's output format, enabling subsequent RL to achieve its performance gains. These findings demonstrates the capability of RL for acquiring generalizable knowledge in complex, multi-modal tasks.",
-            "score": 27,
+            "score": 28,
             "issue_id": 1920,
             "pub_date": "2025-01-28",
             "pub_date_card": {
@@ -70,7 +70,7 @@
             "title": "Optimizing Large Language Model Training Using FP4 Quantization",
             "url": "https://huggingface.co/papers/2501.17116",
             "abstract": "The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit arithmetic operations to reduce these costs. While FP8 precision has demonstrated feasibility, leveraging FP4 remains a challenge due to significant quantization errors and limited representational capacity. This work introduces the first FP4 training framework for LLMs, addressing these challenges with two key innovations: a differentiable quantization estimator for precise weight updates and an outlier clamping and compensation strategy to prevent activation collapse. To ensure stability, the framework integrates a mixed-precision training scheme and vector-wise quantization. Experimental results demonstrate that our FP4 framework achieves accuracy comparable to BF16 and FP8, with minimal degradation, scaling effectively to 13B-parameter LLMs trained on up to 100B tokens. With the emergence of next-generation hardware supporting FP4, our framework sets a foundation for efficient ultra-low precision training.",
-            "score": 12,
+            "score": 13,
             "issue_id": 1920,
             "pub_date": "2025-01-28",
             "pub_date_card": {

log.txt

@@ -1,3 +1,3 @@
-[29.01.2025 21:09] Read previous papers.
-[29.01.2025 21:09] Generating top page (month).
-[29.01.2025 21:09] Writing top page (month).
+[29.01.2025 22:09] Read previous papers.
+[29.01.2025 22:09] Generating top page (month).
+[29.01.2025 22:09] Writing top page (month).