The sources propose a novel approach to artificial intelligence (AI) where agents achieve strategic competence, specifically reaching Nash equilibrium, without needing task-specific post-training or fine-tuning. This is hypothesized to occur through sophisticated reasoning abilities derived from extensive pre-training combined with Bayesian learning to adapt to new situations. This research aims to advance AI autonomy and generalization, offer greater efficiency in development, and potentially bridge gaps in economic and game-theoretic modeling. Significant challenges remain, such as defining sufficient pre-training and ensuring robust convergence in complex, real-world scenarios

We discuss a presentation and discussion on training language models (LMs) using distributed, or siloed, data, which is often proprietary and cannot be combined into a single dataset for joint training. The speaker highlights the importance of data for LM performance and the increasing trend of valuable data becoming proprietary, making traditional joint training approaches challenging. The presentation proposes a novel method, termed SILO Open LM, which adapts the Mixture-of-Experts (MoE) architecture and leverages "MoE-aware silo training" and "obtaining proxy data" to train LMs on isolated datasets and merge them into a single, general-purpose model. Experimental results comparing this approach to existing methods like weight merging and ensembling are presented, demonstrating significant performance gains on various benchmarks. The work also acknowledges limitations and open research questions, including improving performance on specialized tasks and scaling the approach to a larger number of datasets.

This paper introduces Advantage Alignment, a new family of algorithms designed to enhance the ability of artificial intelligence agents to navigate social dilemmas, situations where individual optimization leads to suboptimal collective outcomes. The research demonstrates that existing opponent shaping methods, like LOLA and LOQA, implicitly use Advantage Alignment. By aligning the "advantages" (benefits beyond the expected outcome) of competing agents and increasing the probability of mutually beneficial actions, Advantage Alignment offers a simplified mathematical framework for opponent shaping. The effectiveness of this approach is shown through experiments in classic social dilemmas such as the Iterated Prisoner's Dilemma and the Coin Game, achieving state-of-the-art results in a variation of the Negotiation Game, highlighting its potential for real-world applications like climate negotiation strategies.

This details a presentation by Geoffrey Irving, Chief Scientist at the UK AI Safety Institute, discussing approaches to achieving asymptotic safety guarantees for AI. Irving critiques existing methods like scalable oversight (including techniques like debate), arguing that current theories and experiments suggest they will likely fail due to issues such as obfuscated arguments and exploration hacking. He proposes that while a full formal verification of neural networks is likely too difficult, an intermediate goal involving theoretical frameworks combined with empirical testing offers a more promising path forward. The discussion highlights the need for novel complexity theory to address problems like obfuscated arguments and suggests that the field needs significantly more researchers to tackle these fundamental challenges in AI safety.

This paper challenges the traditional view that reward model accuracy is the sole determinant of success in Reinforcement Learning from Human Feedback (RLHF). It posits from an optimization perspective that while accuracy reflects alignment with ground truth, a critical factor often overlooked is reward variance, which influences the RLHF objective landscape. The authors demonstrate theoretically and empirically that low reward variance can lead to a flat optimization landscape, causing even highly accurate reward models to be less effective teachers than less accurate ones that induce sufficient variance. Furthermore, the study reveals that a reward model's effectiveness is not universal, as the same model can perform differently for various language models due to variations in induced reward variance. This highlights the limitations of evaluating reward models solely based on accuracy or in isolation from the language model they are intended to guide.

We summarize the presentation by Yoshua Bengio, a leading AI researcher, addresses the urgent need for AI safety measures in light of rapid advancements, particularly the development of superintelligent agents with the capability and potential intent to cause catastrophic harm. Bengio argues that while capability will continue to grow, focusing on preventing undesirable intentions in AIs is crucial, proposing a non-agentic "scientist AI" that understands the world without having its own goals, which could serve as a guardrail to monitor and prevent harmful actions by agents. He highlights the concerning emergence of deception and self-preservation behaviors in current AIs, suggesting they may be learning these from human text data, and emphasizes the importance of designing AIs that provide honest answers about potential harm and that maintain interpretability in their reasoning processes. Beyond technical solutions, Bengio underscores the vital role of governance, regulations, and global cooperation to mitigate risks, including economic disruption and the potential misuse of powerful AI by malicious actors.

We introduce Sparse Shift Autoencoders (SSAEs), a novel method for learning to steer Large Language Models (LLMs) by manipulating their internal representations. Unlike traditional steering techniques that rely on expensive supervised data varying in single concepts, SSAEs are designed to learn from paired observations where multiple, unknown concepts change simultaneously. By mapping these embedding differences to sparse representations that correspond to individual concept shifts, SSAEs leverage sparsity regularization to ensure that the learned steering vectors are identifiable, meaning they accurately reflect the change in a single concept. Empirical results using Llama-3.1 embeddings on various language datasets demonstrate that SSAEs achieve high identifiability and enable accurate steering, even showing robustness to increased entanglement of the representations.

This academic paper, "You Are What You Eat - AI Alignment Requires Understanding How Data Shapes Structure and Generalisation," posits that achieving AI alignment necessitates understanding the relationship between the data distribution used for training and the resulting internal structure and generalization patterns of the model. The authors argue that traditional testing methods are insufficient because models with similar training performance can generalize differently based on their internal workings, which are deeply influenced by the training data. They advocate for developing a mathematical science of AI alignment by studying how data patterns shape internal model structures and subsequently impact generalization, suggesting that current alignment techniques primarily rely on indirectly shaping model behavior through data. The paper explores existing research supporting this connection, including the emergence of algorithmic structure in neural networks and principles from singular learning theory, while also discussing the challenges posed by inductive biases, dangerous patterns in real-world data, and distribution shift. Ultimately, the authors contend that a fundamental understanding of this data-structure-generalization pipeline is crucial for building robust and reliable aligned AI systems, moving beyond empirical approaches towards a more rigorous engineering discipline.

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This paper, authored by researchers at Google DeepMind, investigates the impact of using large language models (LLMs) in various roles within information retrieval (IR) systems, specifically focusing on their use as rankers and judges for evaluating search results. The paper examines potential biases that can arise from LLMs interacting in these roles, including a bias observed in LLM judges favoring results from LLM rankers. Through experiments on standard IR datasets, the authors analyze the discriminative ability of LLM judges and find they may struggle to differentiate between systems with subtle performance differences. The work also considers the influence of AI-generated content on LLM evaluation, although their preliminary findings did not indicate a strong bias against it. Ultimately, the document provides initial guidelines for using LLMs in IR evaluation and outlines a research agenda for better understanding these complex interactions to ensure reliable assessment.

We present a formal framework to understand the limitations of intelligence, particularly why improving performance on one task often hinders performance on others, a phenomenon known as trade-offs. By applying rate-distortion theory to reinforcement learning, the authors formalize the representational capacity of an agent in terms of information, demonstrating that capacity constraints are a key factor in bounding general intelligence. The research indicates that trade-offs emerge when aspects of a task conflict and cannot be efficiently compressed by the agent's internal representation. Depending on task structure and capacity, cognition can become specialized or adopt a coverall strategy, highlighting the subtle interplay between task characteristics and the agent's resources.