Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs

• URL: http://arxiv.org/abs/2506.14245v1
• Date: Tue, 17 Jun 2025 07:06:56 GMT
• Title: Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
• Authors: Xumeng Wen, Zihan Liu, Shun Zheng, Zhijian Xu, Shengyu Ye, Zhirong Wu, Xiao Liang, Yang Wang, Junjie Li, Ziming Miao, Jiang Bian, Mao Yang,
• Abstract summary: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for advancing the reasoning capabilities of Large Language Models (LLMs)<n>However, RLVR-tuned models often underperform their base models on the $Pass@K$ metric for solution-finding.<n>We introduce a more precise evaluation metric, $CoT$-$Pass@K$, which mandates that both the reasoning path and the final answer be correct.
• Score: 32.99709073885827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for advancing the reasoning capabilities of Large Language Models (LLMs). However, a critical paradox clouds its efficacy: RLVR-tuned models often underperform their base models on the $Pass@K$ metric for solution-finding, leading to the hypothesis that RLVR merely re-weights existing reasoning paths at the cost of reasoning diversity. In this work, we resolve this contradiction by identifying the source of the problem: the $Pass@K$ metric itself is a flawed measure of reasoning, as it credits correct final answers that probably arise from inaccurate or incomplete chains of thought (CoTs). To address this, we introduce a more precise evaluation metric, $CoT$-$Pass@K$, which mandates that both the reasoning path and the final answer be correct. We provide a new theoretical foundation that formalizes how RLVR, unlike traditional RL, is uniquely structured to incentivize logical integrity. Our empirical results are supportive: using $CoT$-$Pass@K$, we observe that RLVR can incentivize the generalization of correct reasoning for all values of $K$. Furthermore, by analyzing the training dynamics, we find that this enhanced reasoning capability emerges early in the training process and smoothly generalizes. Our work provides a clear perspective on the role of RLVR, offers a more reliable method for its evaluation, and confirms its potential to genuinely advance machine reasoning.

Related papers

• Beyond Correctness: Learning Robust Reasoning via Transfer [51.403609251508904]
  We adopt a simple philosophical view, robust reasoning should remain useful beyond the mind that produced it.<n>We introduce Reinforcement Learning with Transferable Reward, which operationalizes robustness via transfer reward.<n>Our approach improves sampling consistency while improving final answer accuracy, and it reaches comparable performance in substantially fewer training steps.
  arXiv  Detail & Related papers  (2026-02-09T10:41:44Z)
• Reinforced Efficient Reasoning via Semantically Diverse Exploration [73.41112984160992]
  Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs)<n>We propose reinforced efficient reasoning via semantically diverse explorations, i.e., ROSE, for LLMs.<n>Our method incorporates a semantic-entropy-based branching strategy and an $varepsilon$-exploration mechanism.
  arXiv  Detail & Related papers  (2026-01-08T15:56:44Z)
• Limits of Generalization in RLVR: Two Case Studies in Mathematical Reasoning [3.437656066916039]
  Reinforcement with Verifiable Rewards (RLVR) has emerged as a promising approach for enhancing such capabilities.<n>We investigate RLVR on two problems with fully verifiable solutions.<n>We find that RLVR improves evaluation metrics but often by reinforcing superficial Learning metrics rather than acquiring new reasoning strategies.
  arXiv  Detail & Related papers  (2025-10-30T23:16:02Z)
• RAVR: Reference-Answer-guided Variational Reasoning for Large Language Models [21.671577399379885]
  We introduce RAVR, an end-to-end framework that uses answer-conditioned reasoning as a variational surrogate for question-only reasoning.<n> RAVR reduces hesitation, strengthens conclusion consolidation, and promotes problem-specific strategies in reasoning.
  arXiv  Detail & Related papers  (2025-10-29T06:18:37Z)
• PACR: Progressively Ascending Confidence Reward for LLM Reasoning [55.06373646059141]
  We propose Progressively Ascending Confidence Reward (PACR)<n>PACR is a dense, model-intrinsic reward computed directly from the model's evolving belief in the correct answer.<n>Our results suggest that dense, model-intrinsic shaping signals can make RLVR training more effective and reliable.
  arXiv  Detail & Related papers  (2025-10-25T11:25:35Z)
• ConfClip: Confidence-Weighted and Clipped Reward for Reinforcement Learning in LLMs [32.13266235550995]
  Reinforcement learning (RL) has become a standard paradigm for refining large language models (LLMs)<n>Inspired by observations from human learning, we introduce a RL technique that integrates verifiable outcomes with the model's own confidence estimates.
  arXiv  Detail & Related papers  (2025-09-22T13:00:35Z)
• From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR [92.51110344832178]
  Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs)<n>This technical report presents a systematic investigation of exploration capacities in RLVR, covering four main aspects.
  arXiv  Detail & Related papers  (2025-08-11T01:26:16Z)
• Revisiting LLM Reasoning via Information Bottleneck [57.519119962528166]
  Large language models (LLMs) have recently demonstrated remarkable progress in reasoning capabilities through reinforcement learning with verifiable rewards (RLVR)<n>We present a theoretical characterization of LLM reasoning grounded in information bottleneck (IB) principle.<n>We propose IB-aware reasoning optimization (IBRO), a framework that encourages reasoning trajectories to be both informative about the final correct answer and generalizable.
  arXiv  Detail & Related papers  (2025-07-24T13:14:25Z)
• The Invisible Leash: Why RLVR May Not Escape Its Origin [48.915013455847856]
  Recent advances in large reasoning models highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising method for enhancing AI's capabilities.<n>This study presents a theoretical and empirical investigation that provides fresh insights into the potential limits of RLVR.<n>We identify an entropy-reward tradeoff: while RLVR reliably enhances precision, it may progressively narrow exploration and potentially overlook correct yet underrepresented solutions.
  arXiv  Detail & Related papers  (2025-07-20T07:04:08Z)
• RLPR: Extrapolating RLVR to General Domains without Verifiers [103.14103272635893]
  We propose RLPR, a simple verifier-free framework that extrapolates RLVR to broader general domains.<n>We find that addressing the high variance of this noisy probability reward is crucial to make it work.<n>RLPR consistently improves reasoning capabilities in both areas for Gemma, Llama, and Qwen based models.
  arXiv  Detail & Related papers  (2025-06-23T02:56:36Z)
• Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning [87.7836502955847]
  We propose a novel self-rewarding reinforcement learning framework to enhance Large Language Model (LLM) reasoning.<n>Our key insight is that correct responses often exhibit consistent trajectory patterns in terms of model likelihood.<n>We introduce CoVo, an intrinsic reward mechanism that integrates Consistency and Volatility via a robust vector-space aggregation strategy.
  arXiv  Detail & Related papers  (2025-06-10T12:40:39Z)
• Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation [9.282278040339138]
  $textbfR2Rec$ is a reasoning-enhanced recommendation framework.<n>It samples interaction chains from the user-item graph and converts them into structured interaction-of-thoughts.
  arXiv  Detail & Related papers  (2025-06-05T14:16:44Z)
• The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning [43.310209758380886]
  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for training language models (LMs)<n>We decompose the learning signal into reinforcing correct responses and penalizing incorrect ones, referred to as Positive and Negative Sample Reinforcement (PSR and NSR)<n>We show that NSR works by suppressing incorrect generations and redistributing probability mass toward other plausible candidates, guided by the model's prior beliefs.
  arXiv  Detail & Related papers  (2025-06-02T06:10:54Z)
• Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards [11.149294285483782]
  We propose a unified RLVR-based training paradigm that bridges the gap between non-verifiable tasks and verifiable rewards.<n>We introduce a writing-principle-based pairwise Generative Reward Model (GenRM) and a novel Bootstrapped Relative Policy Optimization (BRPO) algorithm.<n>Our approach empowers LLMs to develop robust writing capabilities without supervised fine-tuning.
  arXiv  Detail & Related papers  (2025-05-30T14:34:57Z)
• Learning to Reason without External Rewards [100.27210579418562]
  Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited by reliance on costly, domain-specific supervision.<n>We explore Reinforcement Learning from Internal Feedback (RLIF), a framework that enables LLMs to learn from intrinsic signals without external rewards or labeled data.<n>We propose Intuitor, an RLIF method that uses a model's own confidence, termed self-certainty, as its sole reward signal.
  arXiv  Detail & Related papers  (2025-05-26T07:01:06Z)
• Reinforced Latent Reasoning for LLM-based Recommendation [83.18146814163308]
  Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks.<n>Existing methods typically rely on fine-tuning with explicit chain-of-thought (CoT) data.<n>In this work, we explore an alternative approach that shifts from explicit CoT reasoning to compact, information-dense latent reasoning.
  arXiv  Detail & Related papers  (2025-05-25T11:03:45Z)
• Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary? [60.725923225442095]
  We compare reasoning-based pointwise rerankers (ReasonRR) to standard, non-reasoning pointwise rerankers (StandardRR) under identical training conditions.<n>We find that ReasonRR-NoReason is surprisingly more effective than ReasonRR.
  arXiv  Detail & Related papers  (2025-05-22T16:41:37Z)
• Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? [67.30809748319486]
  Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated notable success in enhancing the reasoning performance of large language models (LLMs)<n>This study critically examines the current state of RLVR.<n>We find that the current training setup does not elicit fundamentally new reasoning patterns.
  arXiv  Detail & Related papers  (2025-04-18T17:59:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.