ℒ_RL[1]hatched band: not updated in this step · solid band: what this stage trainsKimi K3’s report, of this step: partial rollouts, extended · over its token budget a trajectory’s reward is −1 · “forward-only non-policy models” in loss computation

RL step

Objective

tensor

The number this step optimizes; its gradient updates the policy.

shape: $\mathcal L_{\mathrm{RL}}$ [1]

Sources

Contextual evidence from Objective

Kimi K2.5 report §4.4.2, Policy Optimization: for each problem x, K responses are generated using the previous policy π_old; the objective (eq. 1) averages over the N generated tokens of the batch the token-level ratio π_θ/π_old clipped to [α, β], times the reward less the mean reward of the K responses, less τ times the squared log-ratio. The clipping is “a simple gradient masking scheme”: gradients are computed normally for tokens with log-ratios within [α, β] and zeroed outside it, “regardless of the sign of the advantages”. The MuonClip optimizer minimizes this objective.

k25 · arXiv 2602.02276 / KimiK2.5-2602.02276v2.pdf
Pinned revision v2 ↗ · p. 8