Auto. Make Doomgrad HF Review on 29 January

d/2025-01-29.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": {

hf_papers.json

@@ -4,9 +4,9 @@
         "en": "January 29",
         "zh": "1月29日"
     },
-    "time_utc": "2025-01-29 22:09",
+    "time_utc": "2025-01-29 23:09",
     "weekday": 2,
-    "issue_id": 1936,
+    "issue_id": 1937,
     "home_page_url": "https://huggingface.co/papers",
     "papers": [
         {
@@ -218,7 +218,7 @@
             "title": "Open Problems in Mechanistic Interpretability",
             "url": "https://huggingface.co/papers/2501.16496",
             "abstract": "Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific questions about the nature of intelligence. Despite recent progress toward these goals, there are many open problems in the field that require solutions before many scientific and practical benefits can be realized: Our methods require both conceptual and practical improvements to reveal deeper insights; we must figure out how best to apply our methods in pursuit of specific goals; and the field must grapple with socio-technical challenges that influence and are influenced by our work. This forward-facing review discusses the current frontier of mechanistic interpretability and the open problems that the field may benefit from prioritizing.",
-            "score": 7,
+            "score": 8,
             "issue_id": 1920,
             "pub_date": "2025-01-27",
             "pub_date_card": {
@@ -297,7 +297,7 @@
             "title": "Low-Rank Adapters Meet Neural Architecture Search for LLM Compression",
             "url": "https://huggingface.co/papers/2501.16372",
             "abstract": "The rapid expansion of Large Language Models (LLMs) has posed significant challenges regarding the computational resources required for fine-tuning and deployment. Recent advancements in low-rank adapters have demonstrated their efficacy in parameter-efficient fine-tuning (PEFT) of these models. This retrospective paper comprehensively discusses innovative approaches that synergize low-rank representations with Neural Architecture Search (NAS) techniques, particularly weight-sharing super-networks. Robust solutions for compressing and fine-tuning large pre-trained models are developed by integrating these methodologies. Our analysis highlights the potential of these combined strategies to democratize the use of LLMs, making them more accessible for deployment in resource-constrained environments. The resulting models exhibit reduced memory footprints and faster inference times, paving the way for more practical and scalable applications of LLMs. Models and code are available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.",
-            "score": 4,
+            "score": 5,
             "issue_id": 1918,
             "pub_date": "2025-01-23",
             "pub_date_card": {

log.txt

@@ -1,3 +1,3 @@
-[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).
+[29.01.2025 23:09] Read previous papers.
+[29.01.2025 23:09] Generating top page (month).
+[29.01.2025 23:09] Writing top page (month).