Locked at the Entrance, Open Inside: Where RLVR Narrows the Solution Space

arXiv · Qiancheng Zhou, Ruizhe Li · 2026-09-04

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This paper exposes a blind spot in LLM evaluation: current benchmarks miss the *solution structure* gap that makes models fail in the real world.

What it does

The paper critiques existing LLM benchmarks (e.g., MMLU, GSM8K) for evaluating *final answers* without considering the *process* of reaching those answers. It introduces a framework to measure how well models explore the solution space—specifically, whether they prematurely 'lock' into suboptimal paths. The authors show that even state-of-the-art models often fail due to *solution structure* issues (e.g., brute-forcing vs. elegant reasoning), not just lack of knowledge.

Why it matters

For AI practitioners, this is a wake-up call: our benchmarks are optimizing for the wrong thing. If models are 'locked at the entrance'—unable to explore alternative paths—they’ll fail in tasks requiring creativity or multi-step reasoning. It’s a call to design *process-aware* evaluation frameworks that measure *how* models think, not just *what* they conclude.

How it applies

This framework can be used to evaluate agentic systems in planning or tool-use tasks. For example, an agent that *prematurely commits* to a flawed plan (e.g., in code generation or API orchestration) will fail in production. The paper’s insights can guide the design of *solution-space exploration* metrics for agent benchmarks.

Takeaways

• Current benchmarks overlook *solution structure*—focusing on answers, not the *path* to them.
• Models that 'lock' early into suboptimal paths fail due to *process* issues, not knowledge gaps.
• Evaluation frameworks must measure *exploration quality*, not just correctness.
• The paper’s critique applies to agentic systems, where *plan robustness* depends on solution-space diversity.

Topics: evaluation, reasoning, agents, benchmarks