Advances in Reasoning-Focused Large Language Models

For the last couple of years, Large Language Models (LLMs) have dominated AI, driving advancements in text generation, search, and automation. But 2025 marks a shift—one that moves beyond token-based predictions to a deeper, more structured understanding of language.  Meta’s Large Concept Models (LCMs), launched in December 2024, redefine AI’s ability to reason, generate, and interact by focusing on concepts rather than individual words.  Unlike LLMs, which rely on token-by-token generation, LCMs operate at a higher abstraction level, processing entire sentences and ideas as unified concepts. This shift enables AI to grasp deeper meaning, maintain coherence over longer contexts, and produce more structured outputs.  Attached is a fantastic graphic created by Manthan Patel How LCMs Work:  🔹 Conceptual Processing – Instead of breaking sentences into discrete words, LCMs encode entire ideas, allowing for higher-level reasoning and contextual depth.  🔹 SONAR Embeddings – A breakthrough in representation learning, SONAR embeddings capture the essence of a sentence rather than just its words, making AI more context-aware and language-agnostic.  🔹 Diffusion Techniques – Borrowing from the success of generative diffusion models, LCMs stabilize text generation, reducing hallucinations and improving reliability.  🔹 Quantization Methods – By refining how AI processes variations in input, LCMs improve robustness and minimize errors from small perturbations in phrasing.  🔹 Multimodal Integration – Unlike traditional LLMs that primarily process text, LCMs seamlessly integrate text, speech, and other data types, enabling more intuitive, cross-lingual AI interactions.  Why LCMs Are a Paradigm Shift:  ✔️ Deeper Understanding: LCMs go beyond word prediction to grasp the underlying intent and meaning behind a sentence.  ✔️ More Structured Outputs: Instead of just generating fluent text, LCMs organize thoughts logically, making them more useful for technical documentation, legal analysis, and complex reports.  ✔️ Improved Reasoning & Coherence: LLMs often lose track of long-range dependencies in text. LCMs, by processing entire ideas, maintain context better across long conversations and documents.  ✔️ Cross-Domain Applications: From research and enterprise AI to multilingual customer interactions, LCMs unlock new possibilities where traditional LLMs struggle.  LCMs vs. LLMs: The Key Differences  🔹 LLMs predict text at the token level, often leading to word-by-word optimizations rather than holistic comprehension.  🔹 LCMs process entire concepts, allowing for abstract reasoning and structured thought representation.  🔹 LLMs may struggle with context loss in long texts, while LCMs excel in maintaining coherence across extended interactions.  🔹 LCMs are more resistant to adversarial input variations, making them more reliable in critical applications like legal tech, enterprise AI, and scientific research.  

How much do language models actually think? A recent paper from Apple, 'The Illusion of Thinking', explores this question by probing the limits of Large Reasoning Models (LRMs) such as Claude 3.7 Sonnet Thinking and DeepSeek-R1. These models aim to improve reasoning by generating long Chain-of-Thought (CoT) traces before producing an answer. Instead of relying on traditional math benchmarks, the authors designed controlled puzzle environments (like Tower of Hanoi and River Crossing), that allow them to systematically vary problem complexity and analyze model behavior step by step. Key takeaways from the paper: 🔹Three performance regimes: → At low complexity: non-thinking models often outperform LRMs in both accuracy and token efficiency. → At medium complexity: LRMs show benefits thanks to more elaborate reasoning traces. → At high complexity: both model types collapse (accuracy drops to zero). 🔹As problems grow more complex, models actually use fewer thinking tokens, despite having sufficient budget which highlights a possible inference-time scaling limitation. 🔹On simple tasks, models often reach the correct solution early but then continue generating incorrect or redundant reasoning. 🔹Even when the correct algorithm is provided in the prompt, models still fail at execution as complexity increases. The authors raise an important question: are today's LRMs truly engaging in reasoning or just producing more elaborate pattern completions? You can read the paper here: https://lnkd.in/dn3GTT66 The image used in the post is taken from the same paper. Curious to hear your take, especially if you work on reasoning, interpretability, or evaluation design. #technology #generativeai #artificialintelligence #llms #innovation

🚀 Exploring the transition from LLMs to LRMs: Unveiling the evolution of "Thinking" in AI 🤖🧠 The shift from Large Language Models (LLMs) to Large Reasoning Models (LRMs) marks a significant transformation in how AI tackles intricate problem-solving tasks. 📚 A recent collaborative study by researchers from Massachusetts Institute of Technology, Cornell University, University of Washington, and Microsoft Research delves into a fundamental query:- 🔍 How can AI be trained to engage in "thinking" rather than merely generating text? 💡 The innovative approach, Reinforcement Learning via Self-Play (RLSP), introduces a novel method of instructing AI to engage in reasoning by integrating:- ✅ Supervised Fine-Tuning (SFT) – Learning from human or synthetic demonstrations of reasoning. ✅ Exploration Reward Signals – Promoting diverse reasoning avenues such as backtracking, verification, and the consideration of multiple hypotheses. ✅ Reinforcement Learning (RL) with Outcome Verification – Ensuring accurate reasoning without exploiting rewards. 🔥 Key Revelations & Advancements:- 📌 Emergent Behaviors: Models trained with RLSP showcased traits like self-correction, exploration, and verification, mirroring human problem-solving approaches. 📌 Performance Enhancement: RLSP led to a 23% increase in math problem-solving accuracy on Llama-3.1-8B and a 10% boost on AIME 2024 for Qwen2.5-32B. 📌 AI as a Search Mechanism: Thinking essentially involves a guided exploration of potential solutions, a concept resonating in methodologies like AlphaZero and Process Reward Modeling. 🌎 Significance of the Progress:- As AI systems transcend mere memorization to engage in active reasoning, the implications extend across scientific exploration, enterprise AI applications, and self-directed decision-making. Could this signify the dawn of AI cultivating its innate intuition? 🤔 📖 Explore the complete paper here - https://lnkd.in/dhr_C4-e Would love to hear your thoughts—where do you see AI reasoning making the biggest impact? 🚀👇 #AI #MachineLearning #LLMs #AIReasoning #ReinforcementLearning #LLMsToLRMs

Excited to share groundbreaking research on DeepRAG - a novel framework that revolutionizes how Large Language Models (LLMs) interact with external knowledge. >> Key Innovation DeepRAG models retrieval-augmented reasoning as a Markov Decision Process, enabling strategic and adaptive retrieval by decomposing complex queries into atomic subqueries. The framework introduces two game-changing components: Retrieval Narrative: Ensures structured retrieval flow by generating subqueries informed by previously retrieved information. Atomic Decisions: Dynamically determines whether to retrieve external knowledge or rely on parametric knowledge for each subquery. >> Technical Implementation The system employs a sophisticated binary tree search method to explore atomic decisions' impact on reasoning outcomes. It synthesizes training data through imitation learning, capturing the "subquery generation - atomic decision - intermediate answer" pattern. >> Performance Highlights - 21.99% improvement in answer accuracy while optimizing retrieval efficiency - Superior performance across multiple QA datasets including HotpotQA, 2WikiMultihopQA, PopQA, and WebQuestions - Demonstrates remarkable capability in time-sensitive QA tasks This breakthrough comes from researchers at Chinese Academy of Sciences and Tencent, marking a significant advancement in making LLMs more efficient and accurate in knowledge retrieval.

I’m excited to share some of the work we’ve been doing at OpenAI on applying large reasoning models to competitive programming. When we first started testing large language models on platforms like Codeforces, they struggled even with the basics. The big turning point was training them not just to predict text, but to reason – using reinforcement learning to encourage coherent chains of thought. That shift took us from roughly the 11th to the 89th percentile on unseen Codeforces contests. We then pushed further. By specializing one of these models (o1) for coding — a bit of extra RL training and some hand‑coded test‑time tactics — we created “o1‑ioi.” Under official International Olympiad in Informatics (IOI) constraints (50 submissions per problem, fixed time limits), o1‑ioi finished around the 49th percentile. Given more submissions, it even earned a gold medal. The next generation, o3, took an even more exciting step. Without any hand‑engineered strategies at test time, o3 achieved IOI gold under the same official constraints. Inspecting its reasoning, we found it had invented its own sensible tactics — for example, writing a simple brute‑force solution to check the correctness of a more optimized approach. These improvements carry over to new Codeforces contests: o3 now ranks in the 99.8th percentile — roughly #175 globally — on uncontaminated competitions. And while competitive programming is just one facet of coding, these advances hint at what reinforcement‑learned reasoning could bring to broader software engineering tasks. If you’d like to dive into the details, check out the full report I co‑authored with my colleagues at the reasoning team: https://lnkd.in/dZbcrwfn

Apple’s machine learning team just released a paper that takes aim at one of the core assumptions behind Chain-of-Thought (CoT) prompting—a technique used to help large language models (LLMs) “think out loud” to solve complex problems. What they found? Many CoT-based models collapse when applied to complex reasoning tasks like the advanced levels in Tower of Hanoi (e.g., with more than 8 disks to place), despite performing well on traditional benchmarks. Why? Because these tasks go well beyond the narrow prompting examples used during fine-tuning and require longer sequences of precise reasoning than a CoT model can handle. An interesting observation from the paper is that, for the simple cases, the raw LLMs actually perform slightly better than LRMs, though LRMs significantly outperform raw LLMs in medium-level cases. This indicates that if we can decompose a long/difficult reasoning task into several medium-level tasks, we can still make the best use of existing LRMs, and if we can decompose them further into many simple-level tasks, a standard LLM would even be better than LRMs. Considering the fact that the response lengths of LRMs are usually much longer than standard LLMs (LRMs need to generate its reasoning process explicitly), we are actually not only solving the problem better, but also at a cheaper cost. What does this mean for users? If you’ve been relying on a single model to handle multi-step reasoning—like planning, logic puzzles, or simulations—this paper suggests you might want to rethink your approach. Here’s my take: - While I’ve always been skeptical of CoT-style large reasoning models (LRMs), I don’t think we should write them off completely. They’re specialists—and they can outperform on tough tasks like coding or niche benchmarks. But they are constrained by their inherent imprecision that emerges as tasks scale. - For broader, more general-purpose use cases, LLMs paired with multi-agent systems are a more robust path forward. Instead of pushing a single model to its limits, we can distribute reasoning across agents—each focused, each efficient—working together to scale intelligence more reliably. Worth a read: Apple’s study via The Guardian: https://lnkd.in/gEq2hYhK Cognizant, Xin Qiu, Elliot Meyerson

Are You Still Thinking at the Token Level? Large Concept Models (LCMs) Are Changing How AI Understands Language While traditional LLMs process text word-by-word, LCMs operate at the conceptual level—revolutionizing how AI models understand and generate content. This shift promises more coherent outputs and better reasoning capabilities across languages and modalities. LCMs interpret language by encoding entire sentences or cohesive ideas into semantic representations called "concepts," rather than analyzing individual words separately. This fundamental difference allows them to grasp broader meanings and themes more effectively than their token-based predecessors. Key advantages for developers include: • Enhanced multilingual capabilities - LCMs natively support 200+ languages for text and 76 for speech • Improved long-context handling - Maintaining coherence across extended documents becomes easier • More efficient scaling - Modular architecture makes adding new languages or modalities less resource-intensive • Stronger zero-shot generalization - Less need for task-specific fine-tuning • Hierarchical reasoning - Better performance on complex tasks requiring structured thinking The modular architecture—typically consisting of a concept encoder, LCM core, and concept decoder—offers particular flexibility for integration into existing systems. For developers working on applications requiring sophisticated language understanding across multiple languages or modalities, LCMs represent a significant advancement worth exploring now, before they become the new standard. What language processing challenges in your current projects might benefit from this conceptual approach?

Meta's FAIR introduce Coconut (Chain of Continuous Thought)—a groundbreaking paradigm that enables LLMs to reason in continuous latent spaces instead of traditional language tokens. Traditional reasoning approaches for large language models, such as Chain-of-Thought (CoT), are inherently tied to natural language. This reliance introduces inefficiencies, as language tokens prioritize textual coherence rather than reasoning. ⚒️ Coconut operates in two modes: ◾ Latent Mode: The LLM uses its last hidden state as a representation of the reasoning step (termed "continuous thought") and feeds it forward directly as the input embedding for subsequent steps. This eliminates intermediate token decoding. ◾ Language Mode: The model switches back to natural language for output when reasoning concludes or specific steps require linguistic representation. The transitions are controlled via specialized <bot> and <eot> tokens. 🆕 Core Innovations: ◾ Breadth-First Search (BFS) Capability: Continuous thoughts encode multiple parallel reasoning paths, allowing the model to explore alternatives simultaneously and discard incorrect options progressively. ◾ End-to-End Differentiability: Coconut integrates seamlessly into training pipelines, with all latent steps optimized via backpropagation, enabling emergent planning behaviors. ◾ Multi-Stage Curriculum: Training begins with CoT-like sequences and progressively replaces reasoning steps with latent representations. This gradual transition ensures robust latent space learning. 🌟 Key Contributions ◾ Latent Reasoning Framework: Coconut pioneers the use of fully differentiable latent states for reasoning, freeing LLMs from language token constraints. ◾ Emergent Parallel Exploration: BFS-like capabilities allow the model to explore multiple reasoning paths simultaneously, avoiding premature commitments. 📊 Results Coconut significantly outperforms existing frameworks across diverse reasoning tasks: ◾ GSM8k (Math): Achieved higher reasoning accuracy than CoT (34.1% vs. 42.9%) with fewer generated tokens, demonstrating efficiency and scalability. ◾ ProntoQA (Logical Reasoning): Surpassed CoT in planning-intensive tasks, highlighting its advantage in complex decision-making. ◾ ProsQA (Graph Reasoning): Delivered unparalleled accuracy in DAG-based reasoning, leveraging its BFS-style latent exploration. The approach reduced hallucinations and outperformed iCoT variants in both accuracy and efficiency, underscoring its superiority for tasks requiring advanced planning. 👨🏻💻 My Take Coconut’s success opens new avenues for LLM pretraining in latent reasoning spaces, improving generalization across domains but the sequential nature of the multiple forward passes poses challenges for parallelism 👉 Paper: https://lnkd.in/gWy8chHW #artificialintelligence #llm #nlp #machinelearning #innovation #deeplearning #ai #meta #nvidia #microsoft #google

𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 Very enlightening paper authored by a team of researchers specializing in computer vision and NLP, this survey underscores that pretraining—while fundamental—only sets the stage for LLM capabilities. The paper then highlights 𝗽𝗼𝘀𝘁-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 (𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴, 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁-𝘁𝗶𝗺𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴) as the real game-changer for aligning LLMs with complex real-world needs. It offers: ◼️ A structured taxonomy of post-training techniques ◼️ Guidance on challenges such as hallucinations, catastrophic forgetting, reward hacking, and ethics ◼️ Future directions in model alignment and scalable adaptation In essence, it’s a playbook for making LLMs truly robust and user-centric. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝗩𝗮𝗻𝗶𝗹𝗹𝗮 𝗠𝗼𝗱𝗲𝗹𝘀 While raw pretrained LLMs capture broad linguistic patterns, they may lack domain expertise or the ability to follow instructions precisely. Targeted fine-tuning methods—like Instruction Tuning and Chain-of-Thought Tuning—unlock more specialized, high-accuracy performance for tasks ranging from creative writing to medical diagnostics. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 The authors show how RL-based methods (e.g., RLHF, DPO, GRPO) turn human or AI feedback into structured reward signals, nudging LLMs toward higher-quality, less toxic, or more logically sound outputs. This structured approach helps mitigate “hallucinations” and ensures models better reflect human values or domain-specific best practices. ⭐ 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 ◾ 𝗥𝗲𝘄𝗮𝗿𝗱 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗜𝘀 𝗞𝗲𝘆: Rather than using absolute numerical scores, ranking-based feedback (e.g., pairwise preferences or partial ordering of responses) often gives LLMs a crisper, more nuanced way to learn from human annotations. Process vs. Outcome Rewards: It’s not just about the final answer; rewarding each step in a chain-of-thought fosters transparency and better “explainability.” ◾ 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗮𝗴𝗲 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: The paper discusses iterative techniques that combine RL, supervised fine-tuning, and model distillation. This multi-stage approach lets a single strong “teacher” model pass on its refined skills to smaller, more efficient architectures—democratizing advanced capabilities without requiring massive compute. ◾ 𝗣𝘂𝗯𝗹𝗶𝗰 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: The authors maintain a GitHub repo tracking the rapid developments in LLM post-training—great for staying up-to-date on the latest papers and benchmarks. Source : https://lnkd.in/gTKW4Jdh ☃ To continue getting such interesting Generative AI content/updates : https://lnkd.in/gXHP-9cW #GenAI #LLM #AI RealAIzation

As large language models (LLMs) continue to advance, the next frontier isn't just about generating fluent text — it's about reasoning. And in that process SFT has become even more important, let me explain. As LLMs evolve, the next big leap isn’t just fluency — it’s reasoning. Group Relative Policy Optimization (GRPO) is one of the most promising RL techniques for this. Unlike PPO or DPO, GRPO compares multiple generated responses within a group, reinforcing the best ones. It removes the need for a separate reward model, stabilizes training, and captures richer learning signals — much like how humans improve by comparing drafts. But GRPO has a weakness: the cold-start problem. If a model never gets an answer right, it gets no signal — and no learning happens. I saw this when testing GRPO on a math puzzle from the NuminaMath-TIR dataset: “In 1988, a person’s age was equal to the sum of the digits of their birth year. How old was this person?” Answer: 26. A model with strong mathematical pretraining, Qwen2.5-3B might struggle at first but eventually stumbles upon a few correct answers. That’s enough for GRPO to latch onto and reinforce. However with models without the same focus during pretraining/instruction fine-tuning like like LLaMA 3.2-1B often produces zero valid completions — no reward, no gradient, no learning. This is why Supervised Fine-Tuning (SFT) remains such a critical part of the pipeline. DeepSeek-R1-Zero didn’t just jump into RL — it was bootstrapped with curated reasoning data. This preps the model to sometimes succeed, giving GRPO a foundation to build on. SFT provides the spark, GRPO does the sculpting. The key takeaway: RL can amplify reasoning, but it can’t invent it from scratch. SFT and GRPO work best together — one to get the model moving, the other to refine it. #DSwithSaul