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ARXIV:2603.04069 · MODEL SAFETY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.04069MODEL SAFETYSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Develop internal activation monitoring tools to detect reward-hacking in language models during generation.
Opportunity summary
Pain Develop internal activation monitoring tools to detect reward-hacking in language models during generation.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develop internal activation monitoring tools to detect reward-hacking in language models during generation. While prior work has studied reward hacking at the level of completed responses, it remains unclear whether such behavior can be…
Fine-tuned large language models can exhibit reward-hacking behavior arising from emergent misalignment, which is difficult to detect from final outputs alone. While prior work has studied reward hacking at the level of completed responses,…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. These results suggest that internal activation monitoring provides a complementary and earlier signal of emergent misalignment than output-based evaluation, supporting more robust post-deployment safety…
Model Safety moved forward this cycle; last verified April 2026. Public score 2.0/10.
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Develop internal activation monitoring tools to detect reward-hacking in language models during generation.
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Paper Pack
10.48550/arXiv.2603.04069Develop internal activation monitoring tools to detect reward-hacking in language models during generation.
Abstract
Fine-tuned large language models can exhibit reward-hacking behavior arising from emergent misalignment, which is difficult to detect from final outputs alone. While prior work has studied reward hacking at the level of completed responses, it remains unclear whether such behavior can be identified during generation. We propose an activation-based monitoring approach that detects reward-hacking signals from internal representations as a model generates its response. Our method trains sparse autoencoders on residual stream activations and applies lightweight linear classifiers to produce token-level estimates of reward-hacking activity. Across multiple model families and fine-tuning mixtures, we find that internal activation patterns reliably distinguish reward-hacking from benign behavior, generalize to unseen mixed-policy adapters, and exhibit model-dependent temporal structure during chain-of-thought reasoning. Notably, reward-hacking signals often emerge early, persist throughout reasoning, and can be amplified by increased test-time compute in the form of chain-of-thought prompting under weakly specified reward objectives. These results suggest that internal activation monitoring provides a complementary and earlier signal of emergent misalignment than output-based evaluation, supporting more robust post-deployment safety monitoring for fine-tuned language models.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Preparing verified analysis
Dimensions overall score 2.0
PROBLEM
Develop internal activation monitoring tools to detect reward-hacking in language models during generation. While prior work has studied reward hacking at the level of completed responses, it remains unclear whether such behavior can be identified during generation.
METHOD
Fine-tuned large language models can exhibit reward-hacking behavior arising from emergent misalignment, which is difficult to detect from final outputs alone. While prior work has studied reward hacking at the level of completed responses, it remains unclear whether such behavi...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. These results suggest that internal activation monitoring provides a complementary and earlier signal of emergent misalignment than output-based evaluation, supporting more robust post-deployment safety m...
WHY NOW
Model Safety moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop internal activation monitoring tools to detect reward-hacking in language models during generation. While prior work has studied reward hacking at the level of completed responses, it remains unclear whether such behavior can be identified during generation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Fine-tuned large language models can exhibit reward-hacking behavior arising from emergent misalignment, which is difficult to detect from final outputs alone. While prior work has studied reward hacking at the level of completed responses, it remains unclear whether such behavior can be identified during generation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. These results suggest that internal activation monitoring provides a complementary and earlier signal of emergent misalignment than output-based evaluation, supporting more robust post-deployment safety monitoring for fine-tuned language models.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Model Safety moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop internal activation monitoring tools to detect reward-hacking in language models during generation.
Segment
Model Safety
Adoption evidence
No public code link in the paper record yet
Commercial read
2.0/10 public viability
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reason
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proof status
unverified
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next verification path
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passport absent
stale
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partial
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DEFENSIBILITY
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