Model-Task Alignment Drives Distinct RL Outcomes
Recent advances in applying reinforcement learning (RL) to large language
models (LLMs) have led to substantial progress. In particular, a series of
remarkable yet often counterintuitive phenomena have been reported in LLMs,
exhibiting patterns not typically observed in traditional RL settings. For
example, notable claims include that a single training example can match the
performance achieved with an entire dataset, that the reward signal does not
need to be very accurate, and that training solely with negative samples can
match or even surpass sophisticated reward-based methods. However, the precise
conditions under which these observations hold - and, critically, when they
fail - remain unclear. In this work, we identify a key factor that
differentiates RL observations: whether the pretrained model already exhibits
strong Model-Task Alignment, as measured by pass@k accuracy on the evaluated
task. Through a systematic and comprehensive examination of a series of
counterintuitive claims, supported by rigorous experimental validation across
different model architectures and task domains, our findings show that while
standard RL training remains consistently robust across settings, many of these
counterintuitive results arise only when the model and task already exhibit
strong model-task alignment. In contrast, these techniques fail to drive
substantial learning in more challenging regimes, where standard RL methods
remain effective.
68b54fe6851c6e7b001ecaf2
Haoze Wu
claimed_verified
2025-09-02T07:57:01.566Z
657c2a82429a20edb51d2029
https://cdn-avatars.huggingface.co/v1/production/uploads/657c2a82429a20edb51d2029/OkQVj4GAnWzMAkif-mpod.jpeg
Haoze Wu
user
WaitHZ
68b54fe6851c6e7b001ecaf3
Cheng Wang
68b54fe6851c6e7b001ecaf4
Wenshuo Zhao
68b54fe6851c6e7b001ecaf5
Junxian He
Model-Task Alignment Drives Distinct RL Outcomes
Recent advances in applying reinforcement learning (RL) to large language
models (LLMs) have led to substantial progress. In particular, a series of
remarkable yet often counterintuitive phenomena have been reported in LLMs,
exhibiting patterns not typically observed in traditional RL settings. For
example, notable claims include that a single training example can match the
performance achieved with an entire dataset, that the reward signal does not
need to be very accurate, and that training solely with negative samples can
match or even surpass sophisticated reward-based methods. However, the precise
conditions under which these observations hold - and, critically, when they
fail - remain unclear. In this work, we identify a key factor that
differentiates RL observations: whether the pretrained model already exhibits
strong Model-Task Alignment, as measured by pass@k accuracy on the evaluated
task. Through a systematic and comprehensive examination of a series of
counterintuitive claims, supported by rigorous experimental validation across
different model architectures and task domains, our findings show that while
standard RL training remains consistently robust across settings, many of these
counterintuitive results arise only when the model and task already exhibit
strong model-task alignment. In contrast, these techniques fail to drive
substantial learning in more challenging regimes, where standard RL methods
remain effective.
Reinforcement learning applied to large language models shows counterintuitive results that depend on pre-existing model-task alignment, with standard RL methods remaining robust in challenging scenarios.
reinforcement learning large language models Model-Task Alignment pass@k accuracy standard RL training