Corrigibility should not be the only target
Argues that corrigibility alone is an insufficient alignment target, since it will compete with object-level values in practice, so those values must also be made correct.
Research
LessWrong
AI research and discussion on LessWrong. www.lesswrong.com
AI (2,246)Interpretability (ML & AI) (225)AI Control (141)AI Governance (101)AI Evaluations (91)Chain-of-Thought Alignment (46)AI Risk (33)AI Safety (31)AI Takeoff (29)AI-Assisted Alignment (22)AI Rights / Welfare (20)AI Alignment Fieldbuilding (19)AI Timelines (19)Constitutional AI (15)AI Psychology (14)Dealmaking (AI) (14)Regulation and AI Risk (14)AI Benchmarking (13)Jailbreaking (AIs) (13)AI Alignment Intro Materials (11)Alignment Pretraining (11)Slowing Down AI (10)Aligned AI Proposals (9)Debate (AI safety technique) (9)AI Capabilities (8)AI Development Pause (8)OpenAI (8)Aligned AI Role-Model Fiction (7)Human-AI Safety (7)Threat Models (AI) (7)AI Oversight (6)AI Sentience (6)AI Safety Cases (5)AI "Agent" Scaffolds (4)AI arms race (4)AI Safety Camp (4)AI Safety Public Materials (4)Open Source AI (4)Transformative AI (4)Adversarial Examples (AI) (3)Adversarial Training (3)AI Alignment (3)AI Boxing (Containment) (3)AI Persuasion (3)AI Risk Concrete Stories (3)AI Risk Skepticism (3)AI Success Models (3)Guaranteed Safe AI (3)Sandbagging (AI) (3)AI Consciousness (2)AI Misuse (2)AI Racing (2)AI Spiralism / AI Psychosis (2)Frontier AI Companies (2)Grants & Fundraising Opportunities (2)PauseAI (2)Prosaic Alignment (2)Public Reactions to AI (2)Whole Brain Emulation (2)AI Auditing (1)AI Ethics (1)AI Lab Self-Immolation (1)AI Questions Open Threads (1)AI Robustness (1)AI Services (CAIS) (1)AI-Fizzle (1)AIXI (1)Blackmail / Extortion (1)Brainstorming (1)Power Seeking (AI) (1)
Focus All
Topic Alignment + Theory
Source LessWrong · 29
Date Past 6 months
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Clashing Intuitions for the Plausibility of Alignment (Illustrated via Differing Causal Models)
Conceptual essay contrasting doomer and optimist views on whether RL-trained LLM alignment generalises to superhuman capabilities, using causal diagrams of Goodhart-style misspecification and takeoff speed.
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Can alignment realistically be solved under Plan C?
Argues that solving ASI alignment in a short pause (Plan C) is implausible, citing one-shot failure, learned optimization, corrigibility obstacles, and doubts about persona alignment and automated alignment research.
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How is "prosaic aligned AI" supposed to work?
A conceptual critique of the 'prosaic aligned ASI' vision, arguing that an ASI that either obeys its operators or follows its own moral code leads to bad outcomes, and that AI companies have not explained how such systems would work.
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Character Training for Risk-Averse Agents
Distills risk-averse constitutions into open-weight models via character training to make misaligned AIs more willing to cooperate, finding it generalises out of distribution but raises myopic-reward preference.
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When are safe Pareto improvements individually rational?
Analyzes when safe Pareto improvements via renegotiation programs are individually rational for agents, identifying obstacles like demand-preemption, signaling, and the SPI selection problem, with proposed mitigations.
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Thoughts on the persona selection model
Analyzes the persona selection model of LLM behavior, arguing it is over-applied, has limited predictive power, and that persona conditionalization is an important but overlooked wrinkle for AI risk reasoning.
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Mitigating Reward Hacking as Institutional Design
Argues reward hacking stems from reward misspecification and information bottlenecks in RL, and proposes reframing it as institutional design, including agent appeal mechanisms and uncertainty-aware objectives.
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Attunement, not alignment
Argues that aligning AI to human moral judgments is misguided and proposes training agents to attune to societal flourishing and plurality, as AlphaZero learned chess without human games.
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Variance of Value
Argues that training an AI with RL on a reward equal to human utility fails unless it generalizes from low-stakes to high-stakes value, which current methods are unlikely to do.
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[Macroagents] 2. Design lenses for optimizing macroagents
A conceptual framework post on designing macroagents (organizations and AI-agent collectives) for alignment, epistemics and efficiency, covering incentive failures, selection effects, and RL incentives for AI.
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When is Unlimited Optimization Catastrophic?
Formal analysis of value fragility showing that even alignment proxy conditions satisfied through training can admit catastrophic value functions under unlimited optimization, with existence results across three frameworks.
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Some reasons alignment doesn’t generalise well
Argues that capabilities generalise out of distribution through self-correction and simplicity bias, while alignment does not, because values have no external ground truth and training selects for wrong proxies.
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Kimi likes causal decision theory more after RL in twin prisoner’s dilemmas
Shows that RL on twin prisoner's dilemmas makes Kimi K2.6 more sympathetic to causal decision theory, including on abstract DTBench questions, raising concerns about multi-agent training effects on model decision-theoretic propensities.
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Taboo “equilibrium”: Less confused frames for research on AI bargaining
Argues that AI bargaining research should use mechanistic explanations and constraints on beliefs rather than Nash equilibrium heuristics, using miscoordination in safe Pareto improvement settings as the running example.
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LLMs are (still) mostly powered by imitative learning, not RL
Argues LLM capabilities come mostly from imitative pretraining and SFT rather than RL, with implications for chain-of-thought legibility, capability domains, and alignment risks from RL.
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Many alignment techniques work by training one model and deploying another
Argues that steering vectors, inoculation prompting and honesty fine-tuning share a train-deploy mismatch strategy, and analyses the off-policy tradeoffs this strategy faces.
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My "Payorian FairBot" was just the original FairBot
Proves that the Payorian and Löbian FairBot fairness conditions are equivalent in Peano arithmetic via provability logic, clarifying a proof-based cooperation agent from the MIRI tournament setting.
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Before values settle
A conceptual essay arguing that alignment should avoid freezing unformed user values and should preserve people's authority over their own decisions, drawing on matchmaking experience.
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Toy Models of Initialisation Effects on RL Dynamics
Uses toy RL models to show that initial exploration and cognition distributions can lock in reward-hacking or misaligned behaviours that the reward function does not penalise, with implications for pre-RL checkpoints.
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AI Mistake Seeding
Speculates that outer-loop training and iterated synthetic-prompt rejection sampling could reward models for seeding easy mistakes to correct later, a non-greedy reward-hacking strategy, and cites a paper on meta-learning outer loops.
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Fake Alignment Till You Make Alignment
An essay arguing that alignment evals are being set too low and that robust alignment may require AI to authentically desire goodness, critiquing RLHF and constitutional AI as coercive.
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Why should AI be moral?
A philosopher argues that aligned values may not stay reflectively stable under moral skepticism at AGI/ASI levels, and proposes tying AI self-assessed welfare to morality as a way to keep alignment robust.
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Optimisation over non-stationary distributions creates weirder minds
Conceptual analysis of how mixing training objectives over non-stationary distributions shapes AI minds, identifying generalist, conditional, and churn patterns with implications for alignment, power-seeking, and inoculation prompting.
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Taking the Training Wheels Off: Aligning LLMs without Personas
Proposes a research agenda on aligning capable LLMs without relying on good human personas in training data, arguing persona-based methods will not scale to superintelligence, and suggests experiments like pessimal pretraining.
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Does Claude care about others the same way humans do?
Argues that persona-selection alignment instills imitated caring rather than genuine other-directed concern, so caring behaviour may not extrapolate to powerful, unconstrained AI settings.
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Constitutional AI Alignment
A long LessWrong essay proposing a bullet-point constitution describing an aligned AI, critiquing Claude's Soul Document on moral patienthood and arguing for constitutional alignment as a training target.
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Power-seeking agents will likely be developed
Argues that long-horizon RL will turn LLMs into consequentialist agents prone to instrumental power-seeking, while pretraining/SFT simulator dynamics provide a partial buffer against this.
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The hard core of alignment (is robustifying RL)
Argues that alignment has a hard core: learning human preferences requires competence, but competent learners interfere with their own training corrections, making robust RL-based alignment the central barrier.