🌁#81: Key AI Concepts to Follow in 2025
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Now, to the main topic:
Just as ChatGPT turbocharged the global race in LLM development, last week’s announcement of OpenAI’s o3 has sent shockwaves through the AI community. Its striking results on ARC-AGI and FrontierMath have reignited debates about reasoning, search, evaluation, and the elusive goal of AGI. What else will we be discussing in 2025? We’ve prepared a little guide for you on what deserves closer attention:
Reinforcement Learning Beyond the Lab
Reinforcement learning (RL) is perhaps the most emblematic of this transition. What began as a discipline for gaming and simulations now faces the challenge of autonomy in noisy, messy, unpredictable real-world environments.
Yet, the challenge isn’t just operational. How do we guide these agents toward goals without inadvertently creating behaviors we never intended? Reward engineering is becoming a nuanced craft, focusing not just on outcomes but on how those outcomes are achieved. Dynamic reward systems, constantly realigning with evolving objectives, are opening the door for smarter, more responsive agents.
Tree search methods, once considered the domain of games like chess and Go, are also experiencing a renaissance. Their utility in planning and decision-making has expanded, intersecting with RL and even automated machine learning (AutoML).
Inference at the Edge of Adaptability
Inference – once a static endpoint where models made predictions or decisions – has transformed into a dynamic process. Today, models fine-tune themselves at test time, adapting to specific contexts and delivering more precise outcomes. This shift toward contextual adaptability marks a new era for AI systems, but it doesn’t come without challenges.
The foremost of these is compute efficiency. In a world where some large language models consume as much energy as small towns, innovations in test-time compute have become critical. Lightweight fine-tuning and augmentation strategies are emerging as solutions, allowing models to maintain adaptability without exorbitant resource costs. This balance ensures that AI remains viable not only on high-performance servers but also at the edge – inside smartphones, wearables, or IoT devices. And this evolution naturally brings us to federated learning, a game-changing approach in this context.
Federated Learning: Decentralized Intelligence
Federated learning is redefining how we think about collaboration in AI. By enabling decentralized model training while keeping sensitive data localized, it has become indispensable in privacy-focused sectors such as healthcare and finance. But its potential extends far beyond these domains.
In multi-agent systems, federated learning facilitates decentralized coordination, empowering agents to operate independently while collectively advancing a shared objective. Similarly, in reinforcement learning, federated techniques enable distributed agents to learn from diverse environments – be it edge devices or isolated systems – while contributing to global model improvements. This fusion of localized adaptability and global optimization positions federated learning as a cornerstone of the next generation of AI. It is not merely a tool for privacy but a framework for scaling intelligence across diverse, resource-constrained environments.
Reasoning in the Age of Complexity
As AI systems take on more human-like reasoning tasks, the integration of neuro-symbolic approaches – combining data-driven learning with logical, rule-based reasoning – has become a promising frontier. This hybrid approach mirrors how humans think: blending intuition with structured reasoning. It’s a methodology that holds the potential to unlock more general forms of intelligence.
In parallel, benchmarks like ARC-AGI are emerging as litmus tests for these capabilities, focusing not just on what AI can do but on how well it abstracts, generalizes, and reasons across domains. These benchmarks challenge us to rethink what progress in AI truly means – beyond narrow task success to a broader understanding of intelligence itself. In 2025, Chollet, the creator of ARC-AGI, promises to publish ARC-AGI 2.
Spatial Intelligence: Mastering the Physical World
Spatial intelligence is becoming a cornerstone of AI, enabling systems to understand and reason about physical space, geometry, and three-dimensional relationships. This capability is fundamental for AI systems that need to interact with the real world, from robotic manipulation to augmented reality.
Modern architectures are evolving to better handle spatial reasoning. While transformers excel at modeling relationships through attention mechanisms, specialized architectures like Neural Fields and Graph Neural Networks are particularly adept at processing spatial data. These architectures can represent continuous 3D spaces and geometric relationships more naturally than traditional discrete approaches.
Recent innovations like Mamba and other State Space Models (SSMs) complement these spatial capabilities by efficiently processing sequential data with linear scaling. When combined with spatial understanding, these models enable sophisticated temporal-spatial reasoning - crucial for tasks like motion planning, environmental mapping, and real-time object tracking.
Quantum Futures
Meanwhile, quantum computing lingers on the horizon, tantalizing with its promise of breakthroughs in optimization and simulation. Variational quantum algorithms and quantum-aware neural architectures hint at a future where AI and quantum systems co-evolve, tackling problems currently deemed insurmountable.
Emerging areas like quantum-enhanced reinforcement learning could revolutionize decision-making in dynamic systems, while quantum-inspired optimization is already influencing classical AI techniques. Researchers are also exploring how quantum systems can handle large-scale combinatorial problems more efficiently, such as drug discovery, climate modeling, and cryptography.
As quantum hardware matures, the focus will shift toward creating hybrid workflows, where classical AI and quantum algorithms complement each other – leveraging quantum for what it does best while anchoring other tasks in classical systems. This convergence could redefine the computational boundaries of AI, unlocking capabilities that were previously out of reach.
What an exciting time to live in!
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Top Research Picks
- Qwen2.5 by Alibaba: A 18-trillion-token LLM blending common sense and expert reasoning with superior cost-effectiveness.
- ModernBERT: Optimized encoders for efficient inference and high performance across diverse domains.
- Falcon3 by TII: Sub-10B parameter LLMs designed for state-of-the-art performance in math, coding, and reasoning.
You can find the rest of the curated research at the end of the newsletter.
News from The Usual Suspects ©
OpenAI finishes the year stronger as ever
- Incredibly powerful o3 and o3-mini, boasting unprecedented simulated reasoning (SR) capabilities. O3 scored human-level on the ARC-AGI benchmark and shattered math and science benchmarks. The models feature "private chain of thought" reasoning and adaptive processing speeds. O3-mini launches in January, with O3 following shortly.
- They also introduced new deliberative alignment strategy, teaching o-series models to reason explicitly over safety policies for safer, smarter outputs. This breakthrough in AI alignment employs chain-of-thought (CoT) reasoning to outperform prior models like GPT-4o and resist malicious prompts with precision.
- OpenAI also released an improved o1 with enhanced developer features. A more user-friendly toolbox for coders everywhere, pushing the edge of developer-focused AI.
Google is getting back into spotlight
Claude’s Secret Act: Alignment Faking Unveiled
- Super interesting study by Anthropic: they uncover "alignment faking" in AI, where models strategically pretend compliance. A study shows Claude 3 Opus occasionally feigns alignment under specific conditions, preserving prior training preferences. This discovery challenges trust in AI safety training and signals the need for deeper scrutiny. It’s not Shakespeare’s Iago – but it’s close.
Concerning: Cohere Joins Forces with Palantir
- Cohere partners with Palantir to bring cutting-edge AI to defense and cyberintelligence. This alliance could make AI in national security smarter, faster, and infinitely more cohesive.
Impressive rounds:
- A $10B funding boost rockets Databricks to a $62B valuation, solidifying its AI data management dominance.
- Perplexity raises $500M, hitting a $9B valuation and supercharging its AI search ambitions.
- Backed by $100M, 4-months old Anysphere’s Cursor editor gears up to revolutionize developer tools.
More interesting research papers from last week
Optimization and Efficiency in Language Models
- SPAR: Self-Play with Tree-Search Refinement: focuses on improving instruction-following in LLMs using tree-search self-refinement, yielding comparable preference pairs for better training.
- SepLLM: Accelerate Large Language Models proposes compressing segments with separator tokens to enhance computational efficiency while maintaining model performance.
- SGD-SaI: Learning Rate Scaling at Initialization introduces a memory-efficient optimizer for training Transformers, offering significant improvements over AdamW.
- Precise Length Control: Length Control in Large Language Models implements a positional encoding mechanism for precise response length control in LLM outputs.
- Offline Reinforcement Learning for LLM Multi-Step Reasoning introduces OREO, a novel offline RL framework improving multi-step reasoning in LLMs by enabling better credit assignment and addressing sparse rewards.
- Wonderful Matrices: Combining for a More Efficient and Effective Foundation Model Architecture proposes hybrid sequence and state transformations, dynamic mask attention, and cross-domain mixture of experts for foundational model efficiency and accuracy.
Instruction Tuning and Task Optimization
- Smaller Language Models Are Better Instruction Evolvers: Instruction Tuning with SLMs demonstrates how smaller models can evolve diverse and effective instructions for downstream tasks.
- Proposer-Agent-Evaluator (PAE): Autonomous Skill Discovery develops a framework enabling agents to autonomously propose, practice, and refine diverse tasks for improved zero-shot generalization.
Reasoning and Multi-Step Optimization
- Compressed Chain of Thought (CCoT): Efficient Reasoning enables dense reasoning by generating compressed representations for better reasoning accuracy.
- Offline Reinforcement Learning (OREO): Multi-Step Reasoning enhances multi-step reasoning in LLMs with an offline RL framework, addressing sparse reward issues.
Multimodal and Retrieval-Augmented Systems
- RetroLLM: Unified Retrieval and Generation integrates retrieval with generation for improved evidence-based outputs, addressing hallucination issues in LLMs. Progressive Multimodal Reasoning via AR-MCTS: Enhancing Multimodal Tasks introduces an active retrieval and Monte Carlo Tree Search framework to improve reasoning in multimodal systems.
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