Recognition: 2 theorem links
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
Pith reviewed 2026-05-10 23:11 UTC · model grok-4.3
The pith
Reinforcement learning with verifiable rewards improves small-k performance but does not create new reasoning patterns beyond the base model's capabilities.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
RLVR-trained models do not elicit fundamentally new reasoning patterns. While they outperform base models at small k, the base models achieve higher pass@k scores when k is large. Coverage and perplexity analyses show that the observed reasoning abilities originate from and are bounded by the base model. Six popular RLVR algorithms perform similarly and remain far from optimal in leveraging the potential of the base model. Distillation can introduce new reasoning patterns from the teacher and genuinely expand the model's reasoning capabilities.
What carries the argument
Large-k pass@k evaluation together with coverage analysis to determine whether any reasoning pattern lies outside the base model's sampling distribution.
If this is right
- RLVR training does not expand the set of problems an LLM can solve beyond those solvable by its base model.
- Six common RLVR algorithms deliver comparable performance and none fully exploits the base model's latent capabilities.
- Distillation from a stronger teacher model can add reasoning patterns absent from the base model.
- Current RLVR methods fall short of the self-improvement that reinforcement learning is expected to provide for reasoning tasks.
- Paradigms such as continual scaling or multi-turn agent interaction may be needed to move past the base-model bound.
Where Pith is reading between the lines
- Purely sampling-based techniques that avoid RL training altogether could match or exceed RLVR gains on pass@1 without any parameter updates.
- The apparent progress from RLVR on standard benchmarks may largely reflect better exploitation of existing knowledge rather than capability growth.
- Hybrid methods that pair RL with explicit mechanisms to surface low-probability base-model outputs could test whether the current bound is fundamental.
Load-bearing premise
That any reasoning pattern never produced by the base model even after extremely large numbers of samples is genuinely unavailable rather than simply too rare to observe.
What would settle it
An RLVR model correctly solving a problem instance that the matched base model fails to solve after more than one million independent samples would contradict the claim that no new patterns are introduced.
read the original abstract
Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated notable success in enhancing the reasoning performance of large language models (LLMs), particularly on mathematics and programming tasks. Similar to how traditional RL helps agents explore and learn new strategies, RLVR is believed to enable LLMs to continuously self-improve, thus acquiring novel reasoning abilities beyond those of the corresponding base models. In this study we critically examine the current state of RLVR by systematically probing the reasoning capability boundaries of RLVR-trained LLMs across various model families, RL algorithms, and math, coding, and visual reasoning benchmarks, using pass@k at large k values as the evaluation metric. Surprisingly, we find that the current training setup does not elicit fundamentally new reasoning patterns. While RLVR-trained models outperform their base models at small k (e.g., k = 1), the base models achieve a higher pass@k score when k is large. Coverage and perplexity analyses show that the observed reasoning abilities originate from and are bounded by the base model. Treating the base model as an upper bound, our quantitative analysis shows that six popular RLVR algorithms perform similarly and remain far from optimal in leveraging the potential of the base model. By contrast, we find that distillation can introduce new reasoning patterns from the teacher and genuinely expand the model's reasoning capabilities. Overall, our findings suggest that current RLVR methods have not yet realized the potential of RL to elicit truly novel reasoning abilities in LLMs. This highlights the need for improved RL paradigms, such as continual scaling and multi-turn agent-environment interaction, to unlock this potential.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper empirically investigates whether Reinforcement Learning with Verifiable Rewards (RLVR) elicits novel reasoning patterns in LLMs beyond those present in base models. Across multiple model families, six RL algorithms, and benchmarks in math, coding, and visual reasoning, the authors report that RLVR models outperform base models at small k (e.g., k=1) but underperform at large k on pass@k. Coverage and perplexity analyses are used to argue that reasoning abilities originate from and are bounded by the base model distribution. The work contrasts this with distillation, which does expand capabilities, and concludes that current RLVR remains far from optimal in leveraging base-model potential, calling for new paradigms such as continual scaling or multi-turn interactions.
Significance. If the central empirical pattern holds, the result would meaningfully temper claims that RLVR enables self-improvement and discovery of new reasoning strategies in LLMs, instead framing observed gains as reweighting of base-model capabilities. This has clear implications for research on scaling reasoning models. The systematic scope—spanning model families, algorithms, and task types—provides a useful broad empirical baseline, and the explicit comparison to distillation supplies a constructive contrast that highlights where capability expansion does occur.
major comments (2)
- [coverage and perplexity analyses] The load-bearing claim that 'the observed reasoning abilities originate from and are bounded by the base model' (abstract and coverage/perplexity section) rests on large-k pass@k serving as an exhaustive upper bound. To substantiate that RLVR does not elicit new patterns, the analysis must verify that the specific solutions produced by RLVR models appear among base-model samples under identical temperature and decoding settings; higher aggregate pass@k alone does not rule out the possibility that RLVR shifts mass onto low-probability strategies that large-k sampling simply fails to surface. A per-problem overlap metric or explicit recovery check would directly address this.
- [experimental setup and results] The manuscript reports consistent patterns across six RL algorithms but provides no details on statistical significance testing, exact data splits, or whether large-k sampling used identical temperature/decoding settings for base and RLVR models (as noted in the evaluation protocol). These omissions make it difficult to assess whether the reported pass@k gaps are robust or sensitive to sampling variance.
minor comments (3)
- [evaluation metrics] Clarify the precise values of 'large k' employed in the pass@k curves and state the number of independent samples drawn per problem.
- [introduction and abstract] The abstract states that 'six popular RLVR algorithms perform similarly'; the main text should list these algorithms explicitly with citations.
- [figures] Figure captions and axis labels should indicate temperature, top-p, and whether greedy or stochastic decoding was used for the k=1 results.
Simulated Author's Rebuttal
We thank the referee for the constructive comments, which highlight important aspects of our empirical claims. We address each major point below and commit to revisions that strengthen the substantiation of our results without altering the core findings.
read point-by-point responses
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Referee: [coverage and perplexity analyses] The load-bearing claim that 'the observed reasoning abilities originate from and are bounded by the base model' (abstract and coverage/perplexity section) rests on large-k pass@k serving as an exhaustive upper bound. To substantiate that RLVR does not elicit new patterns, the analysis must verify that the specific solutions produced by RLVR models appear among base-model samples under identical temperature and decoding settings; higher aggregate pass@k alone does not rule out the possibility that RLVR shifts mass onto low-probability strategies that large-k sampling simply fails to surface. A per-problem overlap metric or explicit recovery check would directly address this.
Authors: We agree that an explicit per-problem overlap or recovery analysis would provide stronger direct evidence that RLVR solutions lie within the base model's support. While our large-k pass@k results, combined with coverage and perplexity analyses, already indicate that RLVR primarily reweights existing patterns rather than introducing new ones, we will add a recovery check in the revised manuscript. Specifically, we will sample solutions from the base model under identical temperature and decoding settings and report the fraction of RLVR-generated correct solutions that are recoverable in the base model's samples on a per-problem basis. This will directly address the concern about low-probability strategies. revision: yes
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Referee: [experimental setup and results] The manuscript reports consistent patterns across six RL algorithms but provides no details on statistical significance testing, exact data splits, or whether large-k sampling used identical temperature/decoding settings for base and RLVR models (as noted in the evaluation protocol). These omissions make it difficult to assess whether the reported pass@k gaps are robust or sensitive to sampling variance.
Authors: We appreciate this feedback on clarity. The evaluation protocol (Section 3.2) already specifies identical sampling parameters (temperature 0.7, top-p 0.95) for all models, but we will explicitly restate this equivalence for base and RLVR models in the revised text. We will also add details on the standard benchmark test splits used and include statistical significance measures (e.g., standard errors across multiple sampling runs or p-values for key pass@k differences) to demonstrate robustness. These additions will be incorporated without requiring new experiments. revision: yes
Circularity Check
No circularity: purely empirical measurements with no derivation chain
full rationale
The paper reports direct experimental comparisons of pass@k (small and large k) between RLVR models and base models across benchmarks, supplemented by coverage and perplexity measurements. The central claim that reasoning abilities originate from and are bounded by the base model follows from these observed scores rather than any mathematical derivation, fitted parameter renamed as prediction, or self-citation chain. No equations or first-principles steps are present that could reduce to inputs by construction; the work is self-contained against external benchmarks.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption pass@k at sufficiently large k measures the model's total reasoning capacity
Forward citations
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