Reinforcement learning (RL) isn’t new—but as Large Language Models (LLMs) evolve into reasoning machines, RL is taking a central role not just in alignment, but in building reasoning itself. A new survey, “Reinforcement Learning for Large Reasoning Models (LRMs)” by a large group from Tsinghua, Shanghai AI Lab, SJTU, and others, lays out an exhaustive map of the nascent field: what’s working, what’s risky, and what future architects need to solve.
What the survey covers
The paper dives into the core building blocks of using RL in reasoning-centered LLMs (often called LRMs): how to define rewards, what training algorithms are in play, how sampling strategies are evolving, and how infrastructure and task domains factor into the picture. It considers both alignment-adjacent RL (e.g. RLHF, preference learning) and RL whose goal is reasoning performance (accuracy, planning, reflection).
Key themes and insights
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Reward design
The survey classifies rewards into several types:-
Verifiable rewards (e.g. test correctness, unit tests, exact checks) when tasks allow.
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Generative / learned reward models for subjective or open domains.
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Dense rewards vs outcome-only reward schemes—bringing signal into intermediate reasoning steps.
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Unsupervised or weak rewards when neither full correctness metrics nor human feedback are feasible.
The authors emphasize that tasks with strong verifiability tend to yield more reliable RL learning.
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Policy optimization & sampling strategies
There’s a broad sweep of algorithms: policy gradients, off-policy methods, regularized RL, hybrid approaches, critic-based vs critic-free methods. Sampling strategies—how you gather candidate outputs or intermediate chains—have big effects both on performance and on compute cost. Dynamic / structured sampling (e.g. adaptively adjusting paths, beam vs sampling) is becoming more common. -
Foundational problems and gaps
Several of these stand out:-
Distinguishing when RL improves reasoning vs just memorization.
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Balancing weak model priors: does your base LLM already encode reasoning bias, or do you need to train from scratch?
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Trap of over-rewarding narrow achievements; reward hacking.
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Challenges in reward specification in subjective domains.
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Scaling issues: compute, infrastructure, verifying many candidates.
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Training resources & infrastructure
The survey catalogues the spectrum of environments and corpora used: from static datasets to dynamic environments (interactive tasks, tool usage), from single-task to multi-agent setups. It also considers RL frameworks and infrastructure tools (e.g. RL pipeline libraries) that enable reproducible LLM+RL research. -
Applications
RL for LRMs has been used in:-
Coding: unit tests, code correctness, reflection.
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Agentic tasks: agents using tools, web retrieval, planning.
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Multimodal reasoning: vision-language tasks, code+images.
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Robotics / medical / scientific domains. Each has its own reward/verification constraints.
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Why it matters & what to watch next
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Reasoning as an explicit target. RL is being woven into models not just to be more “helpful” or “safe,” but to reason more deeply: plan, reflect, self-correct.
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Verifiability is a power lever. Where tasks allow for exact or semi-exact verification, RL works well. When reward is fuzzy, progress is slower and riskier.
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Cost and scalability are fundamental constraints. As LRMs become larger and used with more test-time compute (more chain-of-thought, more candidate generations), RL training and inference costs balloon; infrastructure and sampling strategy choices can make or break feasibility.
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Hybrid and co-evolving reward models are growing. There’s increasing interest in reward models that both learn and evolve alongside the LLM, or in having the model itself critique or verify its own work.
Takeaways for researchers and builders
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If you’re designing RL for reasoning tasks, aim for verifiable reward signals where possible—they give cleaner gradients and fewer surprises.
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Pay attention to sampling strategy—generating more candidates or reasoning branches helps, but only when combined with selective reinforcement.
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For subjective or “open” tasks (creative writing, alignment, etc.), you likely need sophisticated reward models, rubric-based or generative rewards, and strong regularization.
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Infrastructure matters: your ability to scale RL—from having candidate generation, verifiers, tool execution environments, caching, etc.—significantly affects what you can achieve.
Bottom line: This survey is a timely, comprehensive lookup table for anyone playing at the intersection of LLMs, RL, and reasoning. It confirms that reward design and verifiability are major levers, that RL is now essential for pushing reasoning as a capability, but also that many technical, infrastructural, and algorithmic challenges remain before “reasoning superintelligence.”
Paper link: arXiv 2509.08827 (PDF)
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