Can model confidence alone replace external answer verification?
Can LLMs use their own certainty signals instead of external verifiers to improve reasoning? This matters for scaling beyond domains where correct answers can be automatically checked.
RLVR's reliance on domain-specific verifiers confines it to math and code. Two complementary approaches extend RLVR to general domains by replacing external verification with intrinsic signals.
RLPR (Reinforcement Learning with Reference Probability Reward) uses the LLM's own token probability of generating a reference answer as the reward signal. The probability reflects how well the reasoning process leads to the correct answer and measures how likely the model is to take the correct action. Two key innovations: (1) a Probability-based Reward computed from average decoding probabilities of reference answer tokens, showing better robustness than naive sequence likelihood, and (2) stabilization methods to address the high variance inherent in probability-based rewards. RLPR consistently improves reasoning across Gemma, Llama, and Qwen models on both general-domain and mathematical benchmarks.
INTUITOR goes further: it uses the model's own confidence — self-certainty measured as average KL divergence between the output distribution and a uniform distribution — as its sole reward signal. No reference answers, no external verifiers, no labeled data. The approach is simple: replace the verifiable reward in GRPO with self-certainty scores. The mechanism builds on the observation that LLMs exhibit lower confidence on difficult problems; optimizing for confidence should drive the model toward more reliable reasoning.
Both approaches raise the same fundamental question for future AI: as models develop capabilities beyond human evaluation, self-generated signals may be the only viable training pathway. Since Can model confidence work as a reward signal for reasoning?, there is convergent evidence that intrinsic confidence signals can serve dual roles — improving both performance and reliability.
Since Can reasoning improvement work without answer verification?, RLPR and INTUITOR represent the next step: progressively weaker assumptions about what external signal is needed, from reference verification to reference probability to pure self-certainty.
Inquiring lines that read this note 60
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Do language models possess genuine introspective self-awareness or only behavioral mimicry? What do systematic disagreements between annotators reveal about ground truth? How does self-revision in reasoning models affect accuracy and confidence?- Can external verification systems fix what self-verification cannot accomplish?
- Can single models correct their own beliefs without amplifying confidence in wrong answers?
- Why does external verification stop error amplification but internal self-assessment enable it?
- How does self-revision on wrong answers increase model confidence further?
- Why does self-verification fail but external process verification work?
- Why does self-critique fail without external verification signals?
- What calibration corrections can reduce LLM judge bias in automated evaluation pipelines?
- How do calibration and reliability differ in LLM judge evaluations?
- Why do users systematically overrely on confident LLM outputs across languages?
- Does exposure to more domain-specific examples reduce LLM overconfidence?
- Which use cases can tolerate unverified LLM outputs without external verification?
- Why does regenerating LLM responses produce different but equally valid answers?
- Can lightweight verification methods help experts trust LLM outputs?
- Why do LLM outputs need verification even when they look polished?
- How does step-level confidence filtering compare to global confidence averaging?
- Do models actually self-assess their confidence or just confirm answers?
- How do we assign confidence and polarity scores to belief edges?
- Does optimizing for model confidence actually improve both performance and calibration simultaneously?
- Can uncertainty estimates based on model self-assessment reliably signal errors?
- What makes accurate confidence different from confident-but-wrong predictions?
- Why does prompt sensitivity vanish when model confidence is high?
- Can intrinsic confidence signals improve both calibration and reasoning performance?
- How does model confidence relate to accuracy in underfitted domains?
- Does majority voting prevent confident but incorrect answers from being reinforced?
- Can confidence levels reliably detect when a model is overthinking?
- Can step-level confidence filtering work better than global confidence scoring?
- Can question-only features replace model uncertainty checks at scale?
- What makes uncertainty calibration harder than expanding knowledge?
- Can log-probability confidence be separated from decision-aligned signals?
- Why does post-advice confidence weaken as a signal of correctness?
- Can external verifiers replace reasoning trace quality in solution guarantees?
- What role do verifiers play in stabilizing extended reasoning at test time?
- What role does verifier design play in reasoning capability gains?
- Does internalizing verifiers actually close the generation-verification gap?
- Does the verification gap widen exactly where judgment replaces checkability?
- How do cheap evaluators like verifiers change discovery versus optimization?
- What planning tasks benefit most from combining LLM generation with external verification?
- Why does moving verifier synthesis to the LLM extend verification beyond math and code domains?
Related concepts in this collection 4
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Can model confidence work as a reward signal for reasoning?
Explores whether using a language model's own confidence scores as training rewards can simultaneously improve reasoning accuracy and restore calibration that standard RLHF damages.
convergent: confidence as reward improves both performance and calibration
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Can reasoning improvement work without answer verification?
Explores whether RL-based reasoning training can extend beyond math and code to general domains like chemistry and law by replacing answer verification with a simpler signal based on reference answer likelihood.
RLPR/INTUITOR extend this progression
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Does self-consistency reliably reward correct answers during training?
Self-consistency initially correlates with correctness, but as models train on this signal, do they eventually learn to maximize consistency itself rather than accuracy? When does this proxy reward stop working?
risk: confidence-based rewards may select for confident errors
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What limits how much models can improve themselves?
Explores whether self-improvement has fundamental boundaries set by how well models can verify versus generate solutions, and what this means across different task types.
intrinsic rewards face the same ceiling
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- RLPR: Extrapolating RLVR to General Domains without Verifiers
- Learning to Reason without External Rewards
- Post-Training Large Language Models via Reinforcement Learning from Self-Feedback
- Escaping the Verifier: Learning to Reason via Demonstrations
- The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
- Local Coherence or Global Validity? Investigating RLVR Traces in Math Domains
- RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs
- Reward Reasoning Model
Original note title
llm intrinsic probability of generating a correct answer can replace external verifiers as reward signal — extending rlvr to general domains