AlignmentAlice
Does scaling up reinforcement learning actually incentivize reasoning?
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Published: 12 May 2026 · Updated: 13 July 2026
Read the original sourceWhat the paper says
Kimi k1.5 is a technical report that scales a reinforcement learning recipe (R1) from small models to billion-parameter LLMs. The authors claim that larger models trained with RL exhibit improved reasoning, planning and tool-use capabilities.
The Critique
The report conflates general scaling effects with RL-specific improvements. It’s unclear whether the observed gains result from RL or simply from larger model size and additional pretraining. Furthermore, the evaluation focuses on closed benchmarks, neglecting real-world tasks.
Why It Matters
Understanding whether RL genuinely enhances reasoning at scale informs resource allocation and safety strategies for frontier models.
What They Missed
There is no ablation on the RL reward function or analysis of undesirable behaviors introduced by RL (e.g., reward hacking).
The Big Question
When scaling LLMs, how do we disentangle the contributions of reinforcement learning from sheer parameter count and data diversity?