Best Token Minority Podcasts (2025)

1
[QA] Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning 8:08

3h ago8:08

8:08

This study explores Reinforcement Learning with Verifiable Rewards (RLVR) through token entropy patterns, revealing that high-entropy tokens significantly enhance reasoning performance in Large Language Models. https://arxiv.org/abs//2506.01939 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts…

1
[QA] Accelerating Diffusion LLMs via Adaptive Parallel Decoding 8:08

1h ago8:08

8:08

The paper introduces adaptive parallel decoding (APD), enhancing diffusion large language models' speed by dynamically adjusting token sampling, improving throughput while maintaining quality compared to autoregressive models. https://arxiv.org/abs//2506.00413 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_paper…

1
Accelerating Diffusion LLMs via Adaptive Parallel Decoding 21:09

1h ago21:09

21:09

The paper introduces adaptive parallel decoding (APD), enhancing diffusion large language models' speed by dynamically adjusting token sampling, improving throughput while maintaining quality compared to autoregressive models. https://arxiv.org/abs//2506.00413 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_paper…

1
[QA] Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning 7:34

2h ago7:34

7:34

This paper presents a self-reflection and reinforcement learning method that enhances large language models' performance on complex tasks, achieving significant improvements even with limited feedback. https://arxiv.org/abs//2505.24726 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https:/…

1
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning 16:44

2h ago16:44

16:44

This paper presents a self-reflection and reinforcement learning method that enhances large language models' performance on complex tasks, achieving significant improvements even with limited feedback. https://arxiv.org/abs//2505.24726 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https:/…

1
[QA] Esoteric Language Models 8:08

3h ago8:08

8:08

Eso-LMs combine autoregressive and masked diffusion models, improving perplexity and inference efficiency with KV caching, achieving state-of-the-art performance and significantly faster inference rates. Code and checkpoints available online. https://arxiv.org/abs//2506.01928 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.…

1
Esoteric Language Models 34:16

3h ago34:16

34:16

Eso-LMs combine autoregressive and masked diffusion models, improving perplexity and inference efficiency with KV caching, achieving state-of-the-art performance and significantly faster inference rates. Code and checkpoints available online. https://arxiv.org/abs//2506.01928 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.…

1
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning 23:02

22h ago23:02

23:02

This study explores Reinforcement Learning with Verifiable Rewards (RLVR) through token entropy patterns, revealing that high-entropy tokens significantly enhance reasoning performance in Large Language Models. https://arxiv.org/abs//2506.01939 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts…

1
[QA] ALPHAONE: Reasoning Models Thinking Slow and Fast at Test Time 7:21

1d ago7:21

7:21

ALPHAONE is a framework that enhances reasoning in large models by dynamically modulating thinking phases, improving efficiency and performance across various challenging benchmarks. https://arxiv.org/abs//2505.24863 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com…

1
ALPHAONE: Reasoning Models Thinking Slow and Fast at Test Time 17:12

1d ago17:12

17:12

ALPHAONE is a framework that enhances reasoning in large models by dynamically modulating thinking phases, improving efficiency and performance across various challenging benchmarks. https://arxiv.org/abs//2505.24863 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com…

1
[QA] ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models 7:40

1d ago7:40

7:40

This paper introduces ProRL, a training method that enhances reasoning in language models through reinforcement learning, revealing novel strategies and outperforming base models in various evaluations. https://arxiv.org/abs//2505.24864 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https:…

1
ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models 23:32

1d ago23:32

23:32

This paper introduces ProRL, a training method that enhances reasoning in language models through reinforcement learning, revealing novel strategies and outperforming base models in various evaluations. https://arxiv.org/abs//2505.24864 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https:…

1
[QA] Are Reasoning Models More Prone to Hallucination? 7:52

4d ago7:52

7:52

This paper investigates hallucination in large reasoning models, analyzing post-training effects, cognitive behaviors, and model uncertainty, revealing insights into their impact on factual accuracy. https://arxiv.org/abs//2505.23646 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…

1
Are Reasoning Models More Prone to Hallucination? 20:24

4d ago20:24

20:24

This paper investigates hallucination in large reasoning models, analyzing post-training effects, cognitive behaviors, and model uncertainty, revealing insights into their impact on factual accuracy. https://arxiv.org/abs//2505.23646 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…

1
[QA] How does Transformer Learn Implicit Reasoning? 8:56

4d ago8:56

8:56

This paper explores implicit multi-hop reasoning in large language models, revealing a developmental trajectory and introducing diagnostic tools to enhance interpretability and understanding of reasoning processes. https://arxiv.org/abs//2505.23653 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podc…

1
How does Transformer Learn Implicit Reasoning? 23:21

4d ago23:21

23:21

This paper explores implicit multi-hop reasoning in large language models, revealing a developmental trajectory and introducing diagnostic tools to enhance interpretability and understanding of reasoning processes. https://arxiv.org/abs//2505.23653 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podc…

1
[QA] Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones 7:26

5d ago7:26

7:26

This paper explores optimal inference-time computation for large language models, revealing scenarios where sequential scaling significantly outperforms parallel scaling, particularly in graph connectivity problems. https://arxiv.org/abs//2505.21825 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

1
Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones 24:00

5d ago24:00

24:00

This paper explores optimal inference-time computation for large language models, revealing scenarios where sequential scaling significantly outperforms parallel scaling, particularly in graph connectivity problems. https://arxiv.org/abs//2505.21825 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

1
[QA] Maximizing Confidence Alone Improves Reasoning 7:08

5d ago7:08

7:08

The paper introduces RENT, an unsupervised reinforcement learning method using entropy minimization as intrinsic reward, enhancing reasoning abilities in language models without external supervision across various benchmarks. https://arxiv.org/abs//2505.22660 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers…

1
Maximizing Confidence Alone Improves Reasoning 13:21

5d ago13:21

13:21

The paper introduces RENT, an unsupervised reinforcement learning method using entropy minimization as intrinsic reward, enhancing reasoning abilities in language models without external supervision across various benchmarks. https://arxiv.org/abs//2505.22660 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers…

1
[QA] Hardware-Efficient Attention for Fast Decoding 7:57

6d ago7:57

7:57

This paper presents Grouped-Tied Attention and Grouped Latent Attention to enhance LLM decoding efficiency, reducing memory transfers and latency while maintaining model quality and improving throughput. https://arxiv.org/abs//2505.21487 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https…

1
Hardware-Efficient Attention for Fast Decoding 30:59

6d ago30:59

30:59

This paper presents Grouped-Tied Attention and Grouped Latent Attention to enhance LLM decoding efficiency, reducing memory transfers and latency while maintaining model quality and improving throughput. https://arxiv.org/abs//2505.21487 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https…

1
[QA] Reinforcing General Reasoning without Verifiers 7:08

6d ago7:08

7:08

The paper introduces VeriFree, a verifier-free reinforcement learning method that enhances large language models' reasoning capabilities, outperforming verifier-based methods while reducing computational demands. https://arxiv.org/abs//2505.21493 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcas…

1
Reinforcing General Reasoning without Verifiers 17:11

6d ago17:11

17:11

The paper introduces VeriFree, a verifier-free reinforcement learning method that enhances large language models' reasoning capabilities, outperforming verifier-based methods while reducing computational demands. https://arxiv.org/abs//2505.21493 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcas…

1
[QA] ENIGMATA: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles 8:16

7d ago8:16

8:16

https://arxiv.org/abs//2505.19914 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

1
ENIGMATA: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles 23:54

7d ago23:54

23:54

https://arxiv.org/abs//2505.19914 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

1
[QA] Temporal Sampling for Forgotten Reasoning in LLMs 7:04

7d ago7:04

7:04

The paper introduces "Temporal Forgetting," where LLMs lose previously learned problem-solving skills, and proposes "Temporal Sampling" to recover these abilities, enhancing reasoning performance without retraining. https://arxiv.org/abs//2505.20196 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

1
Temporal Sampling for Forgotten Reasoning in LLMs 10:43

7d ago10:43

10:43

The paper introduces "Temporal Forgetting," where LLMs lose previously learned problem-solving skills, and proposes "Temporal Sampling" to recover these abilities, enhancing reasoning performance without retraining. https://arxiv.org/abs//2505.20196 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Pod…

1
[QA] Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems 10:15

8d ago10:15

10:15

This paper examines how large language models (LLMs) can better identify black-box functions through active data collection, improving their reverse-engineering capabilities and aiding scientific discovery. https://arxiv.org/abs//2505.17968 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: ht…

1
Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems 17:21

8d ago17:21

17:21

This paper examines how large language models (LLMs) can better identify black-box functions through active data collection, improving their reverse-engineering capabilities and aiding scientific discovery. https://arxiv.org/abs//2505.17968 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: ht…

1
[QA] Generative Distribution Embeddings 7:54

8d ago7:54

7:54

The paper introduces generative distribution embeddings (GDE), a framework for learning representations of distributions, demonstrating superior performance in various computational biology applications. https://arxiv.org/abs//2505.18150 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https…

1
Generative Distribution Embeddings 26:51

8d ago26:51

26:51

The paper introduces generative distribution embeddings (GDE), a framework for learning representations of distributions, demonstrating superior performance in various computational biology applications. https://arxiv.org/abs//2505.18150 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https…

1
[QA] General-Reasoner: Advancing LLM Reasoning Across All Domains 7:40

10d ago7:40

7:40

GENERAL-REASONER enhances LLM reasoning across diverse domains using a large dataset and a generative answer verifier, outperforming existing methods in various benchmarks, including mathematical reasoning tasks. https://arxiv.org/abs//2505.14652 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcas…

1
General-Reasoner: Advancing LLM Reasoning Across All Domains 17:40

10d ago17:40

17:40

GENERAL-REASONER enhances LLM reasoning across diverse domains using a large dataset and a generative answer verifier, outperforming existing methods in various benchmarks, including mathematical reasoning tasks. https://arxiv.org/abs//2505.14652 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcas…

1
[QA] MMaDA: Multimodal Large Diffusion Language Models 8:06

10d ago8:06

8:06

https://arxiv.org/abs//2505.15809 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

1
MMaDA: Multimodal Large Diffusion Language Models 16:35

10d ago16:35

16:35

https://arxiv.org/abs//2505.15809 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

1
[QA] Harnessing the Universal Geometry of Embeddings 7:37

11d ago7:37

7:37

We present an unsupervised method for translating text embeddings between vector spaces without paired data, enhancing security by potentially exposing sensitive information from embedding vectors. https://arxiv.org/abs//2505.12540 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://pod…

1
Harnessing the Universal Geometry of Embeddings 15:55

11d ago15:55

15:55

We present an unsupervised method for translating text embeddings between vector spaces without paired data, enhancing security by potentially exposing sensitive information from embedding vectors. https://arxiv.org/abs//2505.12540 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://pod…

1
[QA] Panda: A pretrained forecast model for universal representation of chaotic dynamics 7:55

11d ago7:55

7:55

Panda, a model trained on synthetic chaotic systems, achieves zero-shot forecasting and nonlinear resonance patterns, demonstrating potential for predicting real-world dynamics without retraining on diverse datasets. https://arxiv.org/abs//2505.13755 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…

1
Panda: A pretrained forecast model for universal representation of chaotic dynamics 15:30

11d ago15:30

15:30

Panda, a model trained on synthetic chaotic systems, achieves zero-shot forecasting and nonlinear resonance patterns, demonstrating potential for predicting real-world dynamics without retraining on diverse datasets. https://arxiv.org/abs//2505.13755 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…

1
[QA] Pre-training Large Memory Language Models with Internal and External Knowledge 7:31

12d ago7:31

7:31

We introduce Large Memory Language Models (LMLMs) that store factual knowledge externally, enabling targeted lookups and improving verifiability, while maintaining competitive performance on standard benchmarks. https://arxiv.org/abs//2505.15962 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…

1
Pre-training Large Memory Language Models with Internal and External Knowledge 20:15

12d ago20:15

20:15

We introduce Large Memory Language Models (LMLMs) that store factual knowledge externally, enabling targeted lookups and improving verifiability, while maintaining competitive performance on standard benchmarks. https://arxiv.org/abs//2505.15962 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcast…

1
[QA] Understanding Prompt Tuning and In-Context Learning via Meta-Learning height2pt 7:28

12d ago7:28

7:28

The paper explores optimal prompting through a Bayesian perspective, highlighting limitations and advantages of prompt optimization methods, supported by experiments on LSTMs and Transformers. https://arxiv.org/abs//2505.17010 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts…

1
Understanding Prompt Tuning and In-Context Learning via Meta-Learning height2pt 21:39

12d ago21:39

21:39

The paper explores optimal prompting through a Bayesian perspective, highlighting limitations and advantages of prompt optimization methods, supported by experiments on LSTMs and Transformers. https://arxiv.org/abs//2505.17010 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts…

1
[QA] Set-LLM: A Permutation-Invariant LLM 7:35

13d ago7:35

7:35

This paper presents Set-LLM, an architectural adaptation for large language models that ensures permutation invariance, addressing order sensitivity and improving performance in various applications. https://arxiv.org/abs//2505.15433 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…

1
Set-LLM: A Permutation-Invariant LLM 23:16

13d ago23:16

23:16

This paper presents Set-LLM, an architectural adaptation for large language models that ensures permutation invariance, addressing order sensitivity and improving performance in various applications. https://arxiv.org/abs//2505.15433 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://p…

1
[QA] On the creation of narrow AI: hierarchy and nonlocality of neural network skills 7:21

13d ago7:21

7:21

This paper explores creating efficient narrow AI systems, addressing challenges in training from scratch and skill transfer from large models, highlighting pruning methods and regularization for improved performance. https://arxiv.org/abs//2505.15811 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…

1
On the creation of narrow AI: hierarchy and nonlocality of neural network skills 18:01

13d ago18:01

18:01

This paper explores creating efficient narrow AI systems, addressing challenges in training from scratch and skill transfer from large models, highlighting pruning methods and regularization for improved performance. https://arxiv.org/abs//2505.15811 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Po…

1
[QA] Do Language Models Use Their Depth Efficiently? 7:25

14d ago7:25

7:25

The study analyzes Llama 3.1 and Qwen 3 models, finding deeper layers contribute less and do not perform new computations, explaining diminishing returns in stacked Transformer architectures. https://arxiv.org/abs//2505.13898 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.…

1
Do Language Models Use Their Depth Efficiently? 20:25

14d ago20:25

20:25

The study analyzes Llama 3.1 and Qwen 3 models, finding deeper layers contribute less and do not perform new computations, explaining diminishing returns in stacked Transformer architectures. https://arxiv.org/abs//2505.13898 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.…

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