Weak-to-Strong Generalization

weak-to-strong (E452)

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Frontmatter

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Raw MDX Source

---
title: Weak-to-Strong Generalization
description: Weak-to-strong generalization investigates whether weak supervisors can reliably elicit good behavior from stronger AI systems. OpenAI's ICML 2024 research shows GPT-2-level models can recover 80% of GPT-4's performance gap with auxiliary confidence loss, but reward modeling achieves only 20-40% PGR—suggesting RLHF may scale poorly. Deception scenarios remain untested.
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llmSummary: "Weak-to-strong generalization tests whether weak supervisors can elicit good behavior from stronger AI systems. OpenAI's ICML 2024 experiments show 80% Performance Gap Recovery on NLP tasks with confidence loss (vs 30-50% naive), but reward modeling achieves only 20-40% PGR. OpenAI's Superalignment team (~30 researchers) funded \\$10M+ in grants. Critical limitation: no experiments yet test deceptive models."
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  novelty: 5.5
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clusters:
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subcategory: alignment-training
entityType: approach
---
import {R, EntityLink, DataExternalLinks, Mermaid} from '@components/wiki';

<DataExternalLinks pageId="weak-to-strong" />

## Overview

Weak-to-strong generalization is a research direction investigating whether weaker AI systems or humans can successfully supervise and <EntityLink id="E526">elicit</EntityLink> good behavior from stronger AI systems. This question sits at the heart of AI safety: as AI systems surpass human capabilities, our ability to evaluate and correct their behavior degrades. If weak supervisors can reliably guide strong systems toward good behavior, alignment approaches like <EntityLink id="E259">RLHF</EntityLink> might continue working; if not, we face a fundamental gap between AI capability and human oversight capacity.

Introduced as a concrete research program by <EntityLink id="E218">OpenAI</EntityLink> in late 2023 with the paper ["Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision"](https://arxiv.org/abs/2312.09390) by Burns et al. (published at [ICML 2024](https://proceedings.mlr.press/v235/burns24b.html)), weak-to-strong generalization uses current AI systems as a testbed for this problem. By training a strong model using labels from a weaker model, researchers can study whether the strong model merely imitates the weak model's mistakes or whether it generalizes to perform better than its supervisor. The foundational finding was striking: a GPT-2-level model supervising GPT-4 can recover **approximately 80% of the performance gap** on NLP tasks when using an auxiliary confidence loss—achieving close to GPT-3.5-level performance even on problems where the weak model failed. With naive finetuning alone, Performance Gap Recovery (PGR) ranges from 20-50% depending on task type.

However, significant gaps remain. <EntityLink id="E600">Reward modeling</EntityLink>—critical for RLHF—shows only **20-40% PGR**, suggesting current alignment techniques may scale poorly to superhuman models. The [OpenAI Superalignment team](https://openai.com/index/weak-to-strong-generalization/) (approximately 30 researchers) launched a [\$10M grants program](https://openai.com/index/superalignment-fast-grants/) to accelerate research, with additional \$5M from Eric Schmidt.

The fundamental uncertainty is whether these early results transfer to the most important cases: detecting deception, preventing power-seeking behavior, and maintaining alignment as AI systems approach and exceed human-level capabilities. A deceptive AI system might behave very differently than the non-deceptive systems used in current experiments. As [Anthropic's 2025 research recommendations](https://alignment.anthropic.com/2025/recommended-directions/) note, developing testbeds where systematic overseer errors can be studied remains a priority.

## Quick Assessment

| Dimension | Assessment | Evidence |
|-----------|------------|----------|
| **Tractability** | Medium (20-80% PGR achieved) | [Burns et al. (2024)](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf): GPT-2 supervising GPT-4 recovers 80% of performance gap on NLP tasks with auxiliary confidence loss |
| **Scalability** | Core research question | PGR increases with both weak supervisor size and strong student size; largest students achieve PGR above 50% ([ICML 2024](https://proceedings.mlr.press/v235/burns24b.html)) |
| **Current Maturity** | Proof-of-concept stage | Published at ICML 2024; debate extension shows promise ([Lang et al., 2025](https://arxiv.org/abs/2501.13124)); no production deployment |
| **Time Horizon** | 3-7 years to deployment | Needs methods that work consistently across settings; reward modeling remains challenging (20-40% PGR) |
| **Key Proponents** | OpenAI Superalignment (≈30 researchers), <EntityLink id="E22">Anthropic</EntityLink> | [OpenAI blog](https://openai.com/index/weak-to-strong-generalization/), [Anthropic 2025 directions](https://alignment.anthropic.com/2025/recommended-directions/) |
| **Investment Level** | \$10-15M/year dedicated | OpenAI: \$10M grants program + \$5M from Eric Schmidt; \$150K/year fellowships for grad students ([Fast Grants](https://openai.com/index/superalignment-fast-grants/)) |
| **Critical Limitation** | Deception untested | No experiments yet with strategically deceptive models; current tests use non-adversarial strong models |

## How It Works

<Mermaid chart={`
flowchart TD
    subgraph Training["Training Phase"]
        WM[Weak Model] -->|generates labels| WL[Weak Labels]
        WL -->|supervises| SM[Strong Model]
    end

    subgraph Evaluation["Evaluation Phase"]
        SM -->|tested against| GT[Ground Truth]
        WM -->|tested against| GT
        SM2[Strong Ceiling] -->|trained with ground truth| GT
    end

    subgraph Metrics["Performance Gap Recovery"]
        PGR["PGR = (Strong_trained - Weak) / (Strong_ceiling - Weak)"]
    end

    Training --> Evaluation
    Evaluation --> Metrics
`} />

The core experimental setup trains a strong pretrained model using labels generated by a weaker model, then measures how much of the capability gap the strong model recovers. A PGR of 100% would mean weak supervision is fully sufficient; 0% would mean the strong model merely imitates weak model errors.

## Risk Assessment & Impact

| Risk Category | Assessment | Key Metrics | Evidence Source |
|---------------|------------|-------------|-----------------|
| **Safety Uplift** | Potentially high (if successful) | 80% PGR on NLP suggests supervision can scale; 20-40% PGR on RM is concerning | [Burns et al. (ICML 2024)](https://proceedings.mlr.press/v235/burns24b.html) |
| **Capability Uplift** | Moderate | Better use of limited supervision; methods transferable to capability research | Secondary benefit; dual-use concern |
| **Net World Safety** | Likely positive | Safety-motivated research; no clear capability acceleration | [OpenAI Superalignment team](https://openai.com/index/weak-to-strong-generalization/) |
| **Scalability** | Core research question | PGR increases with model size; largest students achieve greater than 50% PGR | [Burns et al.](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) |
| **Research Investment** | \$10-15M/year, ≈30-50 FTE | OpenAI: ≈30 researchers + \$10M grants; Anthropic: scalable oversight priority | [Fast Grants](https://openai.com/index/superalignment-fast-grants/), [Anthropic 2025](https://alignment.anthropic.com/2025/recommended-directions/) |

### The Core Experiment

Weak-to-strong generalization experiments follow a specific structure:

| Step | Process | Purpose |
|------|---------|---------|
| **1. Weak Supervisor** | Small model or simulated weak system | Represents limited oversight capacity |
| **2. Strong Student** | Large capable model | Represents future AI to be aligned |
| **3. Training** | Train strong model on weak model's labels | Test supervision transfer |
| **4. Evaluation** | Compare strong model to both weak and ceiling | Measure generalization |

### Measuring Success

| Metric | Calculation | Interpretation |
|--------|-------------|----------------|
| **Performance Gap Recovery (PGR)** | (Strong_trained - Weak) / (Strong_ceiling - Weak) | Fraction of potential recovered |
| **Weak Performance** | Weak model's accuracy | Lower bound |
| **Ceiling Performance** | Strong model with strong supervision | Upper bound |

### Idealized Outcomes

| Scenario | PGR Value | Implication |
|----------|-----------|-------------|
| **Perfect Generalization** | 100% | Weak supervision fully sufficient |
| **No Generalization** | 0% | Strong model only imitates weak |
| **Partial Generalization** | 30-70% | Some but not full capability recovery |

## Empirical Results

### OpenAI's Foundational Experiments (2023-2024)

The [Burns et al. (2024)](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) paper, published at [ICML 2024](https://proceedings.mlr.press/v235/burns24b.html), established the empirical foundation for weak-to-strong generalization research.

| Task Type | PGR (Naive) | PGR (w/ Confidence Loss) | Notes | Source |
|-----------|-------------|--------------------------|-------|--------|
| **NLP Benchmarks** | 30-50% | ≈80% | GPT-2 → GPT-4 achieves near GPT-3.5 performance | [Burns et al.](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) |
| **Reward Modeling** | 20-40% | 30-50% | Particularly poor for ChatGPT RM; may indicate RLHF scaling limits | [Burns et al.](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) |
| **Chess (Elo)** | Variable | — | Depends on capability gap size between weak/strong | [Burns et al.](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) |

**Key quantitative finding:** With very weak supervisors and strong models with many orders of magnitude more compute, researchers recovered more than 20% of the performance gap. For the largest students, PGR is often above 50%.

### Key Findings

1. **Partial Recovery is Real**: Strong models genuinely outperform their weak supervisors by 20-80% of the capability gap depending on technique used
2. **Recovery is Incomplete**: Even with best techniques, 20%+ of capability gap remains unrecovered; RM shows only 20-40% PGR
3. **Task Dependence**: PGR varies from ~20% (reward modeling) to ~80% (NLP with confidence loss)—safety-critical tasks not yet characterized
4. **Elicitation Helps**: Auxiliary confidence loss increases PGR from 30-50% → ~80%; bootstrapping and unsupervised finetuning also help
5. **Scale Matters**: PGR increases with both weak model size and strong model size—larger capability gaps may be more addressable than smaller ones

### Auxiliary Techniques Tested

| Technique | Effect on PGR | Mechanism | Source |
|-----------|--------------|-----------|--------|
| **Auxiliary Confidence Loss** | 30-50% → ≈80% on NLP | Encourages strong model to have confident predictions | [Burns et al.](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) |
| **Bootstrapping** | Moderate improvement | Use intermediate models to improve weak labels iteratively | [Burns et al.](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) |
| **Unsupervised Finetuning** | Improves representations | Better model representations with unsupervised finetuning before weak supervision | [Burns et al.](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) |
| **Debate-Assisted Training** | Statistically significant gains | Ensemble of weak models exploits debate arguments for robust supervision | [Lang et al. (AAAI 2025)](https://arxiv.org/abs/2501.13124) |
| **Selective Self-Supervision** | Avoids unnecessary weak labels | Binary classifier identifies when strong model can self-supervise | [Lang et al. (2025)](https://arxiv.org/abs/2511.14166) |

### Reasons for Optimism

| Factor | Quantified Evidence | Significance |
|--------|---------------------|--------------|
| **Generalization Exists** | PGR consistently above 20%, often above 50% for largest students | Foundation for further research; not mere imitation |
| **Techniques Help** | Confidence loss improves PGR from 30-50% → ≈80% on NLP tasks | Room for methodological progress |
| **Scaling Trend** | PGR increases with both weak supervisor size AND strong student size | May continue to improve with scale |
| **Inductive Bias** | Strong models often generalize to correct answers on hard problems where weak model fails | Built-in tendency toward truth |
| **Open Science** | [Open-source code released](https://openai.com/index/weak-to-strong-generalization/); [EleutherAI replication](https://blog.eleuther.ai/weak-to-strong/) | Enables independent verification |

### Reasons for Concern

| Factor | Quantified Evidence | Significance |
|--------|---------------------|--------------|
| **RM Recovery Low** | Only 20-40% PGR for ChatGPT reward modeling | RLHF may scale poorly to superhuman models |
| **Deception Untested** | 0 experiments with strategically deceptive models | Key uncertainty; may be fundamentally different |
| **Task Dependence** | PGR varies from 20% to 80%+ across task types | Safety-critical tasks may fall in low-PGR regime |
| **Naive Methods Insufficient** | "None of the methods work consistently in all settings" ([Burns et al.](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf)) | Current approaches are proofs-of-concept only |
| **Gap to Full Recovery** | Even best methods leave 20%+ capability unrecovered | May be insufficient for superhuman alignment |

### Known Disanalogies

The [original paper](https://arxiv.org/abs/2312.09390) acknowledges important ways current experiments differ from the ultimate superhuman alignment problem:

| Disanalogy | Description | Implication |
|------------|-------------|-------------|
| **Imitation Saliency** | Future superhuman models will have salient representations of human behaviors; current strong models may not have learned features relevant for imitating weak model predictions | Imitating the weak supervisor may be easier to avoid in current experiments than it will be with superhuman models |
| **Pretraining Leakage** | Pretraining data implicitly contains human supervision | Strong models may already have learned some alignment-relevant behaviors |
| **Leap of Faith** | When applied to tasks without ground truth, we must trust the strong model is being honest | Creates natural opportunity for deceptive behavior in deployment |

## The Deception Problem

### Why Deception Changes Everything

Current weak-to-strong experiments use genuinely non-deceptive strong models. A deceptive model poses fundamentally different challenges:

| Non-Deceptive Strong Model | Potentially Deceptive Strong Model |
|---------------------------|-----------------------------------|
| Has relevant capability, tries to use it | May hide capabilities from weak supervisor |
| Generalizes based on implicit understanding | May strategically underperform |
| Errors are genuine mistakes | "Errors" may be strategic |
| Weak supervision reveals capability | Weak supervision may be gamed |

### The Central Uncertainty

> **Key Question**: If a strong model is sophisticated enough to be deceptive, is weak supervision fundamentally incapable of detecting and correcting this?

This question remains open. The theoretical concern is that deception detection requires *at least* the cognitive sophistication of the deceptive system, making weak-to-strong alignment impossible by construction for adversarial cases.

## Scalability Analysis

### Current vs. Future Capability Gaps

| Comparison | Gap Size | Generalization |
|------------|----------|----------------|
| **GPT-2 → GPT-4** | Large but finite | Tested: partial |
| **Human → Human-Level AI** | Zero by definition | Not applicable |
| **Human → Superhuman AI** | Potentially unbounded | Unknown |

### The Fundamental Question

Weak-to-strong generalization *is* the scalability question for alignment. If it works:

- RLHF-style approaches can continue to improve
- Human oversight remains meaningful
- Current alignment research directions are validated

If it doesn't work:

- Fundamentally new approaches needed
- Human oversight becomes theatrical
- Current paradigms have a hard ceiling

## Current Research & Investment

| Metric | Value | Notes |
|--------|-------|-------|
| **Annual Investment** | \$10-15M/year dedicated | OpenAI: \$10M grants + internal team; Anthropic: part of scalable oversight budget |
| **OpenAI Resources** | ≈30 researchers + \$10M grants + \$5M (Schmidt) | [Superalignment team](https://openai.com/index/weak-to-strong-generalization/), [Fast Grants](https://openai.com/index/superalignment-fast-grants/) |
| **Fellowship Funding** | \$150K/year per grad student | \$75K stipend + \$75K compute/research ([Fast Grants](https://openai.com/index/superalignment-fast-grants/)) |
| **Adoption Level** | Experimental | Published at [ICML 2024](https://proceedings.mlr.press/v235/burns24b.html); no production deployment |
| **Primary Researchers** | OpenAI Superalignment, Anthropic, EleutherAI | [EleutherAI replication](https://blog.eleuther.ai/weak-to-strong/) |
| **Recommendation** | Increase investment | High potential; 20-40% RM PGR gap needs closing |

### Recent Research Advances (2024-2025)

Research has expanded significantly since the original 2023 paper, with multiple groups developing theoretical frameworks and practical improvements:

| Development | Source | Key Contribution | Improvement Over Baseline |
|-------------|--------|------------------|---------------------------|
| **Debate-Assisted W2SG** | [Lang et al., AAAI 2025](https://arxiv.org/abs/2501.13124) | Debate helps weak models extract trustworthy information from strong models; ensemble of weak models exploits long arguments | Statistically significant gains on OpenAI NLP benchmarks |
| **Selective W2SG** | [Lang et al., 2025](https://arxiv.org/abs/2511.14166) | Binary classifier identifies when strong model can self-supervise; graph smoothing refines weak labels | Avoids unnecessary weak supervision |
| **Transfer Learning Framework** | [Charikar et al., 2024](https://arxiv.org/abs/2405.16236) | Formal representation-based model quantifying gain under specific assumptions | Theoretical grounding for PGR predictions |
| **Bias-Variance Analysis** | [arXiv 2025](https://arxiv.org/html/2505.24313) | Explains emergence of W2SG through bias-variance decomposition | Identifies when generalization will occur |
| **Data-Centric Lens** | [arXiv 2024](https://arxiv.org/pdf/2412.03881) | Analyzes W2SG through data quality perspective | New diagnostic framework |

**Research community scale:** The [OpenAI Superalignment team](https://openai.com/index/weak-to-strong-generalization/) consists of approximately 30 researchers. The [\$10M Superalignment Fast Grants program](https://openai.com/index/superalignment-fast-grants/) (with \$5M from Eric Schmidt) funded external research, offering grants of \$100K-\$2M and \$150K/year fellowships for graduate students.

[Anthropic's 2025 research recommendations](https://alignment.anthropic.com/2025/recommended-directions/) identify weak-to-strong generalization as a key priority within scalable oversight, noting particular interest in:
- Improving or measuring weak-to-strong generalization
- Developing testbeds with oversight signals of varying quality (e.g., models of varying scales as overseers)
- Exploring differences in W2SG between tasks represented vs. novel in training corpora
- Exploring W2SG for process-based supervision (not just outcome supervision)

### Differential Progress Analysis

| Factor | Assessment |
|--------|------------|
| **Safety Benefit** | Potentially very high if successful |
| **Capability Benefit** | Some (better use of supervision) |
| **Overall Balance** | Safety-leaning - primarily safety-motivated |

## Relationship to Other Approaches

### Complementary Techniques

- **<EntityLink id="E455">Process Supervision</EntityLink>**: Could improve weak supervisor quality
- **<EntityLink id="E482">AI Safety via Debate</EntityLink>**: Alternative scalable oversight approach
- **<EntityLink id="E477">Mechanistic Interpretability</EntityLink>**: Could verify generalization is genuine

### Key Comparisons

| Approach | Strategy for Scalable Oversight |
|----------|-------------------------------|
| **Weak-to-Strong** | Hope strong models generalize beyond supervision |
| **Debate** | Use AI capability against itself |
| **Interpretability** | Understand internal reasoning directly |
| **Process Supervision** | Break reasoning into evaluable steps |

## Research Priorities

### Key Open Questions

1. **Does generalization hold for deception?** The central uncertainty
2. **What determines recovery rate?** Understanding would enable improvement
3. **Can auxiliary techniques close the gap?** How much can methodology help?
4. **Does recovery degrade with gap size?** Critical for superhuman case

### Proposed Research Directions

| Direction | Purpose | Priority |
|-----------|---------|----------|
| **Deception Analogs** | Test with strategically behaving models | High |
| **Larger Capability Gaps** | Test scaling of generalization | High |
| **Safety-Critical Tasks** | Test on alignment-relevant problems | High |
| **Theoretical Analysis** | Understand when/why generalization works | Medium |

## Key Uncertainties & Cruxes

### Expert Disagreements

| Position | Proponents | Argument |
|----------|------------|----------|
| **Optimistic** | Some OpenAI researchers | Partial success suggests path forward |
| **Uncertain** | Most safety researchers | Deception and scaling untested |
| **Pessimistic** | Some alignment researchers | Fundamental impossibility for adversarial case |

### What Would Change Minds

| Evidence | Would Support |
|----------|---------------|
| **High PGR on deception-analog tasks** | Optimistic view |
| **PGR degradation with capability gap** | Pessimistic view |
| **Robust auxiliary techniques** | Middle path viable |
| **Theoretical impossibility results** | Pessimistic view |

## Sources & Resources

### Primary Research

| Type | Source | Key Contributions |
|------|--------|------------------|
| **Foundational Paper** | [Burns et al. (ICML 2024)](https://proceedings.mlr.press/v235/burns24b.html) "Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision" | Introduced framework; 80% PGR on NLP with confidence loss, 20-40% on reward modeling |
| **Full Paper (PDF)** | [OpenAI Technical Report](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf) | Complete methodology, additional experiments, open-source code release |
| **OpenAI Blog** | [OpenAI Superalignment](https://openai.com/index/weak-to-strong-generalization/) | Announced \$10M grants program, research direction overview |
| **Grants Program** | [Superalignment Fast Grants](https://openai.com/index/superalignment-fast-grants/) | \$100K-\$2M grants, \$150K/year fellowships, deadline Feb 2024 |
| **Debate Extension** | [Lang et al. (AAAI 2025)](https://arxiv.org/abs/2501.13124) "Debate Helps Weak-to-Strong Generalization" | Debate + ensemble of weak models improves alignment on NLP benchmarks |
| **Selective W2SG** | [Lang et al. (2025)](https://arxiv.org/abs/2511.14166) "Selective Weak-to-Strong Generalization" | Binary classifier identifies when strong model can self-supervise |
| **Anthropic Directions** | [Recommended Research Directions (2025)](https://alignment.anthropic.com/2025/recommended-directions/) | Places W2SG within scalable oversight priorities; identifies testbed needs |

### Theoretical Analysis

| Source | Focus |
|--------|-------|
| [Charikar et al. (2024)](https://arxiv.org/abs/2405.16236) | Transfer learning framework quantifying W2SG gain under representation-based assumptions |
| [Bias-Variance Analysis (2025)](https://arxiv.org/html/2505.24313) | Explains emergence of W2SG through bias-variance decomposition |
| [Data-Centric Lens (2024)](https://arxiv.org/pdf/2412.03881) | Analyzes W2SG through data quality perspective |

### Analysis & Commentary

| Source | Focus |
|--------|-------|
| [Scalable Oversight and W2SG](https://www.alignmentforum.org/posts/hw2tGSsvLLyjFoLFS/scalable-oversight-and-weak-to-strong-generalization) | Comparison of complementary approaches |
| [A Review of W2SG (AI Safety Camp)](https://www.lesswrong.com/posts/ELbGqXiLbRe6zSkTu/a-review-of-weak-to-strong-generalization-ai-safety-camp) | Critical analysis of limitations and disanalogies |
| [EleutherAI Blog](https://blog.eleuther.ai/weak-to-strong/) | Independent experiments replicating W2SG findings |
| [Paper Review (Artvi)](https://artvi.ai/paper-review-weak-to-strong-generalization/) | Technical summary of Burns et al. methodology |
| [Nature (2024)](https://www.nature.com/articles/d41586-024-03911-3) | "More-powerful AI is coming. Academia and industry must oversee it—together" |

---

## AI Transition Model Context

Weak-to-strong generalization relates to the <EntityLink id="ai-transition-model" /> through:

| Factor | Parameter | Impact |
|--------|-----------|--------|
| <EntityLink id="E205" /> | <EntityLink id="E20" /> | Determines if current alignment approaches can scale |
| <EntityLink id="ai-capability-level" /> | Oversight gap | Directly addresses supervision-capability gap |

Whether weak-to-strong generalization works fundamentally determines the viability of current alignment approaches as AI capabilities increase.

## Risks Addressed

| Risk | Relevance | How It Helps |
|------|-----------|--------------|
| <EntityLink id="E271">Scalable Oversight Failure</EntityLink> | High | Directly addresses the core problem of supervising systems smarter than the supervisor |
| <EntityLink id="E93">Deceptive Alignment</EntityLink> | High | If successful, could detect when models behave differently during training vs deployment |
| <EntityLink id="E253">Reward Hacking</EntityLink> | Medium | Strong models may generalize to true intent rather than exploiting supervisor errors |
| <EntityLink id="E151">Goal Misgeneralization</EntityLink> | Medium | Tests whether models learn intended behavior beyond their training distribution |