NishMath - #llms

Researchers from DeepSeek and Tsinghua University have recently made significant advancements in AI reasoning capabilities. By combining Reinforcement Learning with a self-reflection mechanism, they have created AI models that can achieve a deeper understanding of problems and solutions without needing external supervision. This innovative approach is setting new standards for AI development, enabling models to reason, self-correct, and explore alternative solutions more effectively. The advancements showcase that outstanding performance and efficiency don’t require secrecy.

Researchers have implemented the Chain-of-Action-Thought (COAT) approach in these enhanced AI models. This method leverages special tokens such as "continue," "reflect," and "explore" to guide the model through distinct reasoning actions. This allows the AI to navigate complex reasoning tasks in a more structured and efficient manner. The models are trained in a two-stage process.

DeepSeek has also released papers expanding on reinforcement learning for LLM alignment. Building off prior work, they introduce Rejective Fine-Tuning (RFT) and Self-Principled Critique Tuning (SPCT). The first method, RFT, has a pre-trained model produce multiple responses and then evaluates and assigns reward scores to each response based on generated principles, helping the model refine its output. The second method, SPCT, uses reinforcement learning to improve the model’s ability to generate critiques and principles without human intervention, creating a feedback loop where the model learns to self-evaluate and improve its reasoning capabilities.


References :

• hlfshell: DeepSeek released another cool paper expanding on reinforcement learning for LLM alignment. Building off of their prior work (which I talk about here), they introduce two new methods.
• www.techrepublic.com: Researchers from DeepSeek and Tsinghua University say combining two techniques improves the answers the large language model creates with computer reasoning techniques.

Classification:

• HashTags: #AI #ReinforcementLearning #DeepSeek
• Company: AI Community
• Target: AI Research Community
• Product: LLMs
• Feature: Self-Reflection
• Type: Research
• Severity: Informative

Recent research has revealed that AI reasoning models, particularly Large Language Models (LLMs), are prone to overthinking, a phenomenon where these models favor extended internal reasoning over direct interaction with the problem's environment. This overthinking can negatively impact their performance, leading to reduced success rates in resolving issues and increased computational costs. The study highlights a crucial challenge in training AI models: finding the optimal balance between reasoning and efficiency.

The study, conducted by researchers, tasked leading reasoning LLMs with solving problems in benchmark. The results indicated that reasoning models overthought nearly three times as often as their non-reasoning counterparts. Furthermore, the more a model overthought, the fewer problems it successfully resolved. This suggests that while enhanced reasoning capabilities are generally desirable, excessive internal processing can be detrimental, hindering the model's ability to arrive at correct and timely solutions. This raises questions about how to effectively train models to utilize just the right amount of reasoning, avoiding the pitfalls of "analysis paralysis."


References :

• IEEE Spectrum: It’s Not Just Us: AI Models Struggle With Overthinking
• Composio: CoT Reasoning Models – Which One Reigns Supreme in 2025?

Classification:

• HashTags: #AI #LLMs #Overthinking
• Target: AI Researchers
• Product: LLMs
• Feature: Reasoning in AI
• Type: Research
• Severity: Informative