Autoregressive perception models trained to localize visual entities under the open-vocabulary setting are mostly trained using Supervised fine-tuning (SFT) with maximum likelihood, yet it optimizes a proxy objective (per-token cross-entropy) that is fundamentally misaligned with perception metrics such as precision and recall. In this paper, we explore post-training reinforcement learning (RL), specifically GRPO, to directly align these models with their evaluation metrics. Building up on the recently introduced Falcon Perception, we design an RL framework that addresses perception-specific challenges: reward design for set-structured outputs and multi-head sampling control. We discover multiple benefits from RL for perception: first, RL unlocks state-of-the-art performance in very dense scenes (up to 500 objects per scene), a regime where most existing systems degrade sharply or collapse; furthermore it fixes common issues in autoregressive perception models like mask repetitions and removes almost entirely the need for NMS and coordinate deduplication, which improve both performance and efficiency and remove the need for hyperparameters tuning; overall, we notice improvements on all levels of difficulties in referring expression segmentation (on PBench and SACO-Gold), and we find an elegant way to preserve the knowledge of whether an object exists or not (as evaluated by MCC) without training on negative samples. We show that a simple reward that penalizes false negatives and positives is sufficient. We develop two hybrid self-annotation pipelines, respectively tailored for difficult referring expressions and very dense scenes, and show their benefits on RL-training. Model weights are released as a Falcon Perception revision~\footnote{https://huggingface.co/tiiuae/Falcon-Perception}. Datasets will be published.
Figures & tables
Figure 1: Falcon Perception-HD on very dense scenes. The most impressive effect of RL on top of Falcon Perception is the ability to solve high-density perception at a scale where prior systems degrade or collapse. Top: predictions of the SFT-only baseline (Falcon Perception). Bottom: predictions of Falcon Perception-HD after RL post-training. The base model suffers from low recall and collapse; RL fixes both.
Figure 2: Predictions exceed the annotations. Original training annotations (top) vs. Falcon Perception-HD predictions (bottom).
Table 3
Figure 3: PBench level 4 (relation binding) qualitative results. Falcon Perception (top) vs. Falcon Perception-HD (bottom). RL improves on the hardest compositional referring expressions, where the SFT-only model tends to under/over-segment, miss the correct relational referent, or hallucinate.
Table 5
Model
Coord head
Size head
SFT init (pre-RL)
1.431
2.197
SFT, 1 epoch, same data
1.370 ( −4% )
2.365 ( +8% )
FP-HD (RL)
0.526 ( −63% )
1.729 ( −21% )
Table 9: Mean policy entropy of the coordinate and size heads. The size head is frozen and decoded greedily, yet RL reduces its entropy through the cascade effect. One epoch of SFT on the same data leaves both entropies essentially unchanged.
Figure 8: Rollout samples from intermediate training checkpoints. Each row shows several GRPO rollouts for the same prompt. Rollouts that emit near-duplicate predictions or that under-cover the scene receive negative within-group advantages and are suppressed by the policy gradient; this is the mechanism by which RL eliminates mask repetition (§ 5.3 ) and improves dense-scene recall (§ 5.1 ) without any explicit rule against either failure mode.
Figure 9: Final dense-scene annotations. Examples of the human-verified dense annotations used as ground truth during RL post-training.
Figure 10: Final hard-referring-expression annotations. Each example shows the rollout selected by the GPT-5 judge as the ground-truth target for a prompt where the greedy prediction of Falcon Perception failed.
Model
COCO-dense
LVIS-dense
SAM 3
43.5
31.5
Baseline FP
55.1
33.6
FP-HD (Ours)
58.1
40.5
Appendix
Table 15: External dense benchmarks. Left: mask F 1 on COCO-dense (category-as-query, ≥5 instances, 1,974 queries) and LVIS-dense ( 882 queries, >25 instances). Right: box F 1 @0.5 on Dense200.
Model
no NMS
with NMS
MRR
LocateAnything-3B [ 48 ]
35.9
36.6
28.5%
Rex-Omni-3B [ 14 ]
—
37.4
—
Falcon Perception (pre-RL)
67.9
68.0
2.1%
FP-HD (Ours)
73.9
73.9
0.4%
Appendix
Table 16: Box F 1 @0.5 on the PBench dense split. Ground-truth boxes are derived from the PBench dense masks; the protocol is identical for all models. NMS yields only marginal improvements on the redundant baselines, while Falcon Perception-HD does not need it.
k
Falcon Perception (AR)
SAM 3 (DETR)
deterministic
72.1
58.4
pass@2
72.7
54.5
pass@4
76.8
55.7
pass@8
79.0
56.8
Δ (pass@8 − deterministic)
+6.9
−3.4
Appendix
Table 17: pass@ k on the PBench dense split. Stochastic sampling improves over the deterministic prediction for the autoregressive model, which motivates RL post-training, but degrades the DETR-style SAM 3 under the per-query Bernoulli policy.
Figure 11: Training curves. Top: total reward, precision, recall, mean matched IoU, and F 1 . Bottom: per-head policy entropy (LM, coordinate, size) and within-group advantage standard deviation. The size head is frozen and sampled greedily, yet its entropy decreases in lockstep with the two trainable heads, an in-training manifestation of the cascade effect of § 3.1 .