Large language models rely on inference-time context including rationales and instructions to improve task performance. Prior work attempts to internalize such benefits via parameter-efficient fine-tuning, and also studies inference-time representation interventions, but these approaches typically update the base model or learn fixed directions tied to global attributes. As a result, it remains unclear how to capture and reuse the internal effect of a helpful context that is specific to a given query. We take a representation-level view and define a context imprint as the hidden-state displacement between context-conditioned and query-only forward passes. We propose HintSteer, which trains a lightweight query-conditioned predictor to estimate the imprint from the query representation and injects it into a frozen model at inference time, approximating the missing context effect without explicitly providing context. Experiments on mathematical reasoning and writing demonstrate the effectiveness of imprint-based steering under no-context inference and indicate that both imprint magnitude and predictability influence the resulting gains.

Generating Scalable Vector Graphics (SVG) code from natural-language instructions is an open-ended task without absolute visual ground truth, leaving both evaluation and policy optimization without a faithful signal. Scalar metrics (CLIP, Aesthetic) calibrated on natural images transfer poorly to stylized vector content, and reusing them as RL rewards triggers reward hacking. We address both limitations with rubric-based scoring. We first establish empirically that prompting a vision–language judge with a multi-axis rubric correlates with human judgments far better than scalar metrics, both across samples and within instructions. Building on this finding, we introduce **RULER** (Instance-aware **Ru**bric Rewards for Reinforcement **Le**a**r**ning), which converts each instruction into an instance-aware rubric of six items spanning semantic, visual, and stylistic axes; a judge VLM scores rendered rollouts item-by-item, and the weighted satisfactions form a fine-grained reward optimized via Group Relative Policy Optimization. Because the rubric is derived from text alone, RULER requires neither paired SVG ground truth nor human preference labels. On MMSVG-Illustration and MMSVG-Icon, RULER lifts the rubric score from 0.432/0.395 to 0.693/0.683, surpassing dedicated SVG specialists and matching the substantially larger DeepSeek-V3, with ablations identifying rubric design as the active lever for RL on open-ended SVG generation.