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  3. Why Self-Rewarding Works: Theoretical Guarantees for Iterati
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Why Self-Rewarding Works: Theoretical Guarantees for Iterative Alignment of Language Models

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Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Why Self-Rewarding Works: Theoretical Guarantees for Iterative Alignment of Language Models

PDF: https://arxiv.org/pdf/2601.22513v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Why Self-Rewarding Works: Theoretical Guarantees for Iterative Alignment of Language Models

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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

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Reinforcement Learning via Self-Distillation
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Self-Improvement of Large Language Models: A Technical Overview and Future Outlook
Score 3.0up
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Real-Time Aligned Reward Model beyond Semantics
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Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models
Score 5.0up
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One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment
Score 8.0up
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Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop
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Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
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RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
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