I briefly reviewed the paper "SFT Memorizes, RL Generalizes," which compares SFT and RL in post-training of LLM/VLM from HKU, UC Berkeley, Google DeepMind, and New York University
The conclusion suggests SFT excels in memorization, while RL is better for generalization. However, since LLM/VLM should benefit humans beyond just generalization, a mix of SFT and RL is advisable. Typically, some SFT is followed by RL to understand prompt formats and enhance generalization through trial and error.
The study focused on one model, Llama-3.2-Vision-11B, using environments like General Points for arithmetic reasoning and V-IRL for spatial reasoning. Training data was used for both SFT and RL, with evaluations on in-distribution and out-of-distribution data to assess memorization and generalization.
I want to apply RL extensively, but it requires building a similar simulation environment. For domain-specific models, significant investment in creating a "playground" for the model is crucial, as the effort will directly influence the outcomes.
Prompt : Using HTML, CSS, and JavaScript in a single HTML file to create a simulation of the solar system. Pay extreme attention to the UI to make it as intuitive as possible. Ensure that every planet appears as a sphere and is labeled with its corresponding name.
The OpenAI o3-mini model is a significant improvement over the o1-mini, reaching o1 performance levels. While generally good, its performance isn't universally better than previous models (o1, o1-prev.) or GPT-4o across all benchmarks. This means workflows should be re-evaluated with each model upgrade.
The o3-mini has "low," "medium," and "high" versions, with "low" being the base model used for benchmarking. It's speculated that the higher versions simply involve more processing. A fair comparison with other models like Gemini 2.0 Thinking or DeepSeek-R1 would likely need to use the "low" version and a similar "think more" mechanism.
The system card is recommended reading due to its comprehensive benchmark data.
Anthropomorphic reasoning about neuromorphic AGI safety
Summary of "Anthropomorphic Reasoning About Neuromorphic AGI Safety" This paper explores safety strategies for neuromorphic artificial general intelligence (AGI), defined as systems designed by reverse-engineering essential computations of the human brain. Key arguments and proposals include:
1. Anthropomorphic Reasoning Validity: - Neuromorphic AGI’s design and assessment rely on human cognition models, making anthropomorphic reasoning (using human-like traits) critical for safety analysis. Comparisons to human behavior and neural mechanisms provide insights into AGI behavior and risks.
2. Countering Safety Criticisms: - The authors challenge claims that neuromorphic AGI is inherently more dangerous than other AGI approaches. They argue all AGI systems face intractable verification challenges (e.g., real-world unpredictability, incomputable action validation). Neuromorphic AGI may even offer safety advantages by enabling comparisons to human cognitive processes.
3. Motivational Architecture: - Basic drives (e.g., curiosity, social interaction) are essential for cognitive development and safety. These pre-conceptual, hardwired drives (analogous to human hunger or affiliation) shape learning and behavior. The orthogonality thesis (intelligence and goals as independent) is contested, as neuromorphic AGI’s drives likely intertwine with its cognitive architecture.
4. Safety Strategies: - **Social Drives**: Embedding drives like caregiving, affiliation, and cooperation ensures AGI develops prosocial values through human interaction. - **Bounded Reward Systems**: Human-like satiation mechanisms (e.g., diminishing rewards after fulfillment) prevent extreme behaviors (e.g., paperclip maximization). - **Developmental Environment**: Exposure to diverse, positive human interactions and moral examples fosters
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Simple summary on DeepSeek AI's Janus-Pro: A fresh take on multimodal AI! It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications: ✦ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data. ✦ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
Evolution and The Knightian Blindspot of Machine Learning
The paper discusses machine learning's limitations in addressing Knightian Uncertainty (KU), highlighting the fragility of models like reinforcement learning (RL) in unpredictable, open-world environments. KU refers to uncertainty that can't be quantified or predicted, a challenge that RL fails to handle due to its reliance on fixed data distributions and limited formalisms.
### Key Approaches:
1. **Artificial Life (ALife):** Simulating diverse, evolving systems to generate adaptability, mimicking biological evolution's robustness to unpredictable environments. 2. **Open-Endedness:** Creating AI systems capable of continuous innovation and adaptation, drawing inspiration from human creativity and scientific discovery.
3. **Revising RL Formalisms:** Modifying reinforcement learning (RL) models to handle dynamic, open-world environments by integrating more flexible assumptions and evolutionary strategies.
These approaches aim to address ML’s limitations in real-world uncertainty and move toward more adaptive, general intelligence.
Artificial Kuramoto Oscillatory Neurons (AKOrN) differ from traditional artificial neurons by oscillating, rather than just turning on or off. Each neuron is represented by a rotating vector on a sphere, influenced by its connections to other neurons. This behavior is based on the Kuramoto model, which describes how oscillators (like neurons) tend to synchronize, similar to pendulums swinging in unison.
Key points:
Oscillating Neurons: Each AKOrN’s rotation is influenced by its connections, and they try to synchronize or oppose each other. Synchronization: When neurons synchronize, they "bind," allowing the network to represent complex concepts (e.g., "a blue square toy") by compressing information. Updating Mechanism: Neurons update their rotations based on connected neurons, input stimuli, and their natural frequency, using a Kuramoto update formula. Network Structure: AKOrNs can be used in various network layers, with iterative blocks combining Kuramoto layers and feature extraction modules. Reasoning: This model can perform reasoning tasks, like solving Sudoku puzzles, by adjusting neuron interactions. Advantages: AKOrNs offer robust feature binding, reasoning capabilities, resistance to adversarial data, and well-calibrated uncertainty estimation. In summary, AKOrN's oscillatory neurons and synchronization mechanisms enable the network to learn, reason, and handle complex tasks like image classification and object discovery with enhanced robustness and flexibility.
New look for AI powered paper reviews from the list by Hugging Face Daily Papers ( managed by the @akhaliq )
Bookmark the webpage along, check comprehensive reviews by Google DeepMind Gemini 1.5, and listen to audio podcast made by the same tech used in NotebookLM. Link: https://deep-diver.github.io/ai-paper-reviewer/
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Simple summarization of Evolving Deeper LLM Thinking (Google DeepMind)
The process starts by posing a question. 1) The LLM generates initial responses. 2) These generated responses are evaluated according to specific criteria (program-based checker). 3) The LLM critiques the evaluated results. 4) The LLM refines the responses based on the evaluation, critique, and original responses.
The refined response is then fed back into step 2). If it meets the criteria, the process ends. Otherwise, the algorithm generates more responses based on the refined ones (with some being discarded, some remaining, and some responses potentially being merged).
Through this process, it demonstrated excellent performance in complex scheduling problems (travel planning, meeting scheduling, etc.). It's a viable method for finding highly effective solutions in specific scenarios.
However, there are two major drawbacks: 🤔 An excessive number of API calls are required. (While the cost might not be very high, it leads to significant latency.) 🤔 The evaluator is program-based. (This limits its use as a general method. It could potentially be modified/implemented using LLM as Judge, but that would introduce additional API costs for evaluation.)
Simple Summarization on DeepSeek-R1 from DeepSeek AI
The RL stage is very important. ↳ However, it is difficult to create a truly helpful AI for people solely through RL. ↳ So, we applied a learning pipeline consisting of four stages: providing a good starting point, reasoning RL, SFT, and safety RL, and achieved performance comparable to o1. ↳ Simply fine-tuning other open models with the data generated by R1-Zero (distillation) resulted in performance comparable to o1-mini.
Of course, this is just a brief overview and may not be of much help. All models are accessible on Hugging Face, and the paper can be read through the GitHub repository.
Understanding and Benchmarking Artificial Intelligence: OpenAI's o3 Is Not AGI
It's an interesting paper that argues "new approaches are required that can reliably solve a wide variety of problems without existing skills." "It is therefore hoped that the benchmark outlined in this article contributes to further exploration of this direction of research and incentivises the development of new AGI approaches that focus on intelligence rather than skills."
📝ChemQwen-vL is a vision-language model fine-tuned based on the Qwen2VL-2B Instruct model. It has been trained using the International Chemical Identifier (InChI) format for chemical compounds and is optimized for chemical compound identification. The model excels at generating the InChI and providing descriptions of chemical compounds based on their images. Its architecture operates within a multi-modal framework, combining image-text-text capabilities. It has been fine-tuned using datasets from: https://iupac.org/projects/
❤️🔥Stranger Zone's MidJourney Mix Model Adapter is trending on the Very Model Page, with over 45,000+ downloads. Additionally, the Super Realism Model Adapter has over 52,000+ downloads, remains the top two adapter on Stranger Zone! strangerzonehf/Flux-Midjourney-Mix2-LoRA, strangerzonehf/Flux-Super-Realism-LoRA
