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arxiv: 2607.06522 · v1 · pith:ZK6IRPDT · submitted 2026-07-07 · cs.AI · cs.CV

Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment

Han-Jun Ko , Jr-Jen Chen , Haobo Yuan , Hsin-Ying Lee , Tiancheng Shen , Ming-Hsuan Yang , Yu-Chiang Frank Wang This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-07-08 02:53 UTCglm-5.2pith:ZK6IRPDTrecord.jsonopen to challenge →

classification cs.AI cs.CV
keywords vision-language modelsphysical reasoningchain-of-thought alignmentreinforcement learningreward designcross-task generalizationPHYREsymbolic grounding
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The pith

Aligning reasoning to physical outcomes lets 8B model beat expert agent

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that vision-language models fail at physical reasoning for two reasons: their chain-of-thought reasoning contradicts physical reality (hallucinated CoT), and their stated reasoning disconnects from their actual actions (reasoning-action misalignment). Both failures share a root cause — the model's reasoning is never checked against what actually happens after it acts. The authors propose VAORA, a reward design that projects the model's reasoning trace and visual observations into a shared symbolic space — object positions, collision events, placement descriptions — and rewards the model when its reasoning matches both the initial scene and the post-action outcome. A grounding reward checks whether the model accurately perceives the starting scene; a collision reward checks whether the model's predicted collision events match what the simulator actually produces; a placement reward checks whether the model's described ball placement matches where the ball actually lands. These visual-action alignment rewards are gated by a success-probability estimate from a pretrained DQN expert, so reasoning quality is only rewarded when the action itself is physically plausible. The authors train an 8B VLM solely on the PHYRE physics benchmark and show it surpasses the DQN expert on unseen PHYRE task types, transfers zero-shot to a different physics simulator (Virtual Tool) at the level of frontier closed-source models, and improves causal reasoning on a visual question-answering benchmark — all from a single training environment.

Core claim

The central discovery is that supervising the alignment between a VLM's reasoning trace and the actual visual consequences of its actions — not just task success — is what enables cross-task and cross-environment generalization. The paper provides direct evidence through reward breakdown analysis: supervised fine-tuning transfers static scene perception but fails on placement and collision reasoning; success-driven RL collapses into shortcut visual-to-action mappings that abandon grounded reasoning entirely. Only when both the initial-scene alignment and the post-action outcome alignment are jointly rewarded does the model develop reasoning that generalizes to unseen tasks, a different simor

What carries the argument

VAORA's mechanism is a symbolic projection-and-compare pipeline: the model's reasoning trace is parsed into structured symbolic tuples (object positions on a 3×3 spatial grid, predicted collision events with contact points and directions, placement descriptions relative to reference objects), and the simulator's ground-truth states are extracted into the same symbolic format. Consistency between the two is measured by soft grid scores and coordinate distances, producing dense reward signals that directly supervise whether the model's reasoning matches physical reality. The visual-action alignment rewards are gated by a DQN-derived success probability to ensure reasoning is only rewarded when

If this is right

  • If grounding reasoning to post-action outcomes is the key to generalization, then any interactive reasoning task — robotic manipulation, navigation, tool use — could benefit from the same symbolic-alignment reward pattern, not just physics puzzles.
  • The finding that success-driven RL collapses into shortcut mappings suggests that reward shaping for VLMs in continuous-action spaces requires outcome-level supervision, not just task-success signals, to avoid degenerate solutions.
  • The zero-shot transfer from PHYRE to Virtual Tool implies that aligned physical reasoning is at least partially environment-agnostic — the model learns transferable causal concepts rather than simulator-specific visual correlations.
  • The improvement on CRAFT VQA from training only on PHYRE suggests that grounding reasoning to action outcomes induces broader causal understanding that extends beyond action selection to passive reasoning and question answering.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The restriction of the collision reward to first-collision-only events means the generalization claim is validated on a structurally constrained subset of tasks (10 of 25 PHYRE types). Tasks requiring multi-step collision chains are excluded from held-out testing, so the generalization advantage may not extend to tasks with longer causal chains.
  • The symbolic extraction functions are hand-designed for 2D physics puzzles with discrete object types and simple collision dynamics. Whether the same approach scales to 3D environments, deformable objects, or fluid dynamics — where symbolic state extraction is far less tractable — remains an open question.
  • The reliance on a pretrained DQN expert for dense reward estimation creates a ceiling: the VLM cannot be rewarded for actions the DQN considers unlikely to succeed, potentially limiting exploration beyond the expert's competence distribution.
  • The single-turn, single-action setting means the model cannot learn from failed attempts. Extending to multi-turn interaction — where the model observes outcomes and updates its reasoning — could substantially expand the range of generalizable physical concepts, as the authors themselves note.

Load-bearing premise

The collision reward only models the first object the placed ball collides with, which means the held-out testing set is restricted to 10 of 25 PHYRE task types where the key physical interaction is a single direct collision. The generalization claim is validated only on tasks matching this structural constraint; tasks requiring multi-step collision chains are excluded from evaluation.

What would settle it

If one tested VAORA on the 15 excluded PHYRE task types — those requiring multi-step collision chains — and found that the alignment rewards provided no signal and performance degraded to baseline levels, the central claim that outcome alignment enables generalization would be significantly weakened.

Figures

Figures reproduced from arXiv: 2607.06522 by Han-Jun Ko, Haobo Yuan, Hsin-Ying Lee, Jr-Jen Chen, Ming-Hsuan Yang, Tiancheng Shen, Yu-Chiang Frank Wang.

Figure 1
Figure 1. Figure 1: Two major obstacles in CoT-based physical reasoning. Hallucinated CoT denotes the model producing physically incorrect reasoning that leads to wrong action; Misaligned Action, on the other hand, bypasses physically-aligned reasoning via a visual shortcut and results in wrong action planning. Our VAORA aims to resolve both issues, generating successful action with proper physical reasoning. Prior work shows… view at source ↗
Figure 2
Figure 2. Figure 2: Framework. VAORA consists of two components: (a) Visual-Alignment Reward, which anchors reasoning to action-independent visual context. (b) Gated Visual-Action Alignment Reward, which aligns reasoning with the visual outcome of the model’s actions, gated by a success probability estimated by an expert model. 2 Related Work Interactive Physical Reasoning. The paradigm for interactive physical reasoning reli… view at source ↗
Figure 3
Figure 3. Figure 3: Generalization of VAORA. We train VAORA on Phyre tasks and evaluate across three scenarios: (a) Phyre test set for cross-task generalization, (b) Virtual-Tool for cross-engine generalization, and (c) CRAFT VQA to assess the transferability of learned reasoning to visual question answering. (see Appendix Sec. B.4 for details), which bridges open-vocabulary reasoning back to the low-level coordinate space re… view at source ↗
Figure 4
Figure 4. Figure 4: Overview of all 25 task types in the PHYRE benchmark, arranged in row-major order from task type [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The 3 × 3 spatial grid defined over the full scene for the grounding reward. Each region corresponds to one of the nine textual spatial labels. For placement grounding, the 3 × 3 grid is defined relative to a reference object obj c (i) , centered on that object with each cell sized proportionally to the object’s bounding box. The label ˆℓ (i) thus describes where the placed ball is located relative to the … view at source ↗
Figure 6
Figure 6. Figure 6: The 3 × 3 spatial grid defined relative to the green ball (highlighted in green) for the placement reward. Each region describes the spatial relationship of the placed ball with respect to the reference object. coordinate (ˆcx, cˆy) and a ground-truth label ℓ with grid coordinate (cx, cy), the soft-grid score is defined as: soft(ˆℓ, ℓ) = 1 − p (ˆcx − cx) 2 + (ˆcy − cy) 2 2 √ 2 (7) The denominator 2 √ 2 cor… view at source ↗
Figure 7
Figure 7. Figure 7: PHYRE qualitative example. The initial scene (top-left) shows a green ball resting on a left platform and a blue jar positioned to its right. The model correctly identifies the scene configuration, reasons that placing the red ball at the upper-left of the green ball will strike it rightward, and predicts the action [51, 39, 23]. The simulation frames confirm that the reasoning trace is action-aligned: the… view at source ↗
Figure 8
Figure 8. Figure 8: Virtual-Tool qualitative example. The initial scene shows a green ball sitting inside a gray trapezoid container, with a blue target line at the bottom of the scene. The model correctly reasons to drop a ball onto the trapezoid container to tip it, releasing the green ball toward the target. Frames 0–4 confirm the causal chain: the action ball falls and strikes the right side of the trapezoid (Frames 1, 2)… view at source ↗
Figure 9
Figure 9. Figure 9: CraftVQA qualitative example. The scene contains a brown triangle resting on a right-side platform, a gray jar (basket) at the bottom center, and several other dynamic objects (red ball, cyan ball, purple ball, brown ball) on various platforms. The question asks: “If any of the other objects are removed, will the small brown triangle fall into the basket?” The model correctly answers true, reasoning that w… view at source ↗
read the original abstract

Vision-language models (VLMs) struggle to generalize in interactive physical reasoning, particularly under unseen tasks and environments. Two key failure modes are prominent: hallucinated chain-of-thought (CoT) reasoning that contradicts physical reality, and misalignment between the model's reasoning and actions. We present VAORA (Visual Action Outcome Reasoning Alignment), a novel reward design that directly addresses both issues. VAORA introduces two complementary rewards: Visual Alignment Reward, which anchors VLM reasoning to the visual context independent of the agent action itself, and Visual-Action Alignment Reward, which grounds reasoning in the visual outcome induced by the model's action. Together, these rewards suppress hallucinated CoT and reduce the gap between reasoning and behavior. To improve training stability, we further employ smooth, dense rewards by estimating success probabilities using a pre-trained in-domain expert agent. Experiments on PHYRE and Virtual Tool support our performances across novel-task and unseen-environment settings, confirming that grounded and generalizable physical intelligence can be induced through VAORA.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 7 minor

Summary. The paper introduces VAORA (Visual Action Outcome Reasoning Alignment), a dual-reward framework for training VLMs on interactive physical reasoning tasks. The approach combines a Visual Alignment Reward (grounding reasoning to the initial scene) with a Visual-Action Alignment Reward (grounding reasoning to post-action visual outcomes), using a pre-trained DQN expert to provide smooth, dense success-probability estimates that stabilize RL training. Experiments span three settings: cross-task generalization on PHYRE (held-out task types), cross-environment zero-shot transfer from PHYRE to Virtual Tool, and broader physical understanding on CRAFT VQA. The central claim is that an 8B model trained solely on PHYRE with VAORA surpasses the DQN expert on unseen tasks, matches frontier closed-source models on Virtual Tool, and improves causal reasoning on CRAFT VQA.

Significance. The paper addresses a genuine gap: connecting CoT reasoning in VLMs to physical outcomes via reward signals, rather than relying on SFT imitation or sparse task-success RL. The dual-reward decomposition (grounding, placement, collision) is well-motivated, and the reward breakdown analysis (Table 4) and component ablation (Table 5) provide useful mechanistic insight into why each reward matters. The cross-environment transfer result (Table 2) and the CRAFT VQA gains (Table 3) are notable demonstrations that the learned reasoning extends beyond the training simulator. The approach is falsifiable: the ablation cleanly isolates each reward component, and the three-split protocol provides replicability. The commitment to releasing datasets, checkpoints, and code is a strength.

major comments (3)
  1. Appendix B.2 discloses that the collision reward r_C (Eq. 4) models only the first object the red ball collides with, restricting the held-out testing pool to 10 of 25 PHYRE task types. The central cross-task generalization claim—surpassing the DQN expert on unseen PHYRE tasks—is therefore validated only on tasks where the key physical interaction is the first collision event. For the 15 excluded task types requiring multi-step collision chains, r_C provides no training signal and the generalization claim is untested. This is load-bearing for the headline claim in §1 ('surpassing the DQN expert on unseen PHYRE tasks') and should be explicitly qualified in the main text (not only in the appendix). The paper should state the restriction in §4.2 and frame the claim as applying to first-collision-dominated tasks.
  2. The three testing splits (Appendix B.2) share substantial overlap: task types 00000, 00001, 00002, and 00003 each appear in two or more splits. The paper frames the three splits as verifying 'stability across different held-out task distributions,' but the overlap means the splits test largely the same task structures with different training partitions. This provides weaker evidence of robustness than three genuinely disjoint splits would. The paper should either (a) acknowledge this overlap and reframe the stability claim accordingly, or (b) construct at least one fully disjoint split from the 10 eligible task types to demonstrate that performance is not an artifact of particular task-type selection.
  3. §3.2, Eq. (2): the symbolic extraction functions phi(r) and psi(o)/psi(v) are described as 'general' but are instantiated only for the PHYRE domain (3x3 spatial grid, first-collision extraction). The cross-environment transfer to Virtual Tool (Table 2) and the CRAFT VQA results (Table 3) presumably reuse the same phi/psi, but the paper does not describe how these functions are adapted for environments with different object types, action spaces, or physics. Since the cross-environment claim is a central contribution, the paper should clarify whether phi/psi are reused as-is or adapted, and what the adaptation entails.
minor comments (7)
  1. Table 1: the DQN-expert row shows Testing Set 2 Pass@1 = 0.260, while +EG+VAORA achieves 0.198 on the same split. The claim of 'surpassing the DQN expert across nearly all metrics' should be qualified to note that on Testing Set 2, VAORA does not surpass the DQN on Pass@1.
  2. §4.3, Table 2: the Virtual Tool evaluation protocol is modified (tool selection removed, colors remapped). While these adaptations are reasonable, they change the benchmark's original difficulty. The paper should note this explicitly in the main text, not only in Appendix A.2.
  3. Table 3: the CRAFT VQA gains over +EG are modest (Overall: 44.56 to 46.06, a 1.5-point improvement). The paper should discuss whether this is within noise bounds, given that only 1,000 questions per category are sampled.
  4. §3.3: the reward composition r_DQN * (1 + r_P + r_C) is described, but the choice of the additive form (1 + r_P + r_C) versus a multiplicative or normalized form is not justified. A brief note on why this particular composition was chosen, or whether alternatives were tried, would strengthen the method section.
  5. Appendix B.4, Eq. (8): the penalty constraint w_c * p_c = 0.1 is presented as ensuring uniform penalty across reward types. This is a reasonable design choice but is presented without justification for why 0.1 is the right scale. A brief sensitivity note would strengthen the method.
  6. The paper uses future-dated references (e.g., Qwen3-VL, Gemini 3.1, GPT-5.4, Claude Sonnet 4.6). If these are real models available at submission time, this is fine; if any are hypothetical or projected, this should be disclosed.
  7. Figure 2 caption references '(a)' and '(b)' components but the figure layout could be clearer. A brief label on each subfigure would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for a careful and constructive report. The referee correctly identifies three issues: (1) the collision reward's first-collision limitation restricts the held-out testing pool to 10 of 25 PHYRE task types, which is not stated in the main text; (2) the three testing splits share substantial task-type overlap, weakening the robustness claim; and (3) the symbolic extraction functions phi/psi are described as general but instantiated only for PHYRE, with no explanation of how they are adapted for Virtual Tool and CRAFT VQA. We agree with all three points and will revise the manuscript accordingly. Below we address each comment in detail.

read point-by-point responses

  1. Referee: Appendix B.2 discloses that the collision reward r_C models only the first object the red ball collides with, restricting held-out testing to 10 of 25 PHYRE task types. The central cross-task generalization claim is therefore validated only on first-collision-dominated tasks. This should be explicitly qualified in the main text, not only in the appendix.

    Authors: The referee is correct on both the factual point and its implications for the headline claim. The restriction to 10 of 25 task types is currently disclosed only in Appendix B.2, while the main text (§1, §4.2) states the generalization claim without qualification. This is a genuine gap between what we claim and what we evaluate. We will revise the manuscript as follows: (1) In §4.2, we will explicitly state that the held-out testing pool is restricted to the 10 task types where the key physical interaction is the first collision event triggered by the red ball, and that the remaining 15 task types requiring multi-step collision chains are excluded because r_C provides no training signal for them. (2) In §1, we will qualify the headline claim from 'surpassing the DQN expert on unseen PHYRE tasks' to 'surpassing the DQN expert on unseen PHYRE tasks where the key physical interaction is the first collision event.' (3) We will add a sentence in the Limitation section noting that extending r_C to model multi-step collision chains is necessary to evaluate generalization on the remaining 15 task types. We agree this is load-bearing for the headline claim and should not be buried in the appendix. revision: yes

  2. Referee: The three testing splits share substantial overlap: task types 00000, 00001, 00002, and 00003 each appear in two or more splits. The paper frames the splits as verifying stability across different held-out distributions, but the overlap means the splits test largely the same task structures. The paper should either acknowledge the overlap and reframe the stability claim, or construct at least one fully disjoint split.

    Authors: The referee's observation is accurate. Examining the three splits: Testing Set 1 = {00001, 00002, 00003, 00006, 00013}; Testing Set 2 = {00000, 00001, 00003, 00012, 00014}; Testing Set 3 = {00000, 00001, 00002, 00009, 00015}. Task types 00000, 00001, 00002, and 00003 each appear in two splits, meaning the splits share substantial task-type overlap and do not constitute three genuinely independent evaluations of robustness. We will take option (a): acknowledge the overlap and reframe the stability claim. Specifically, in §4.2 and Appendix B.2, we will reframe the three-split protocol as verifying 'consistency across different train/test partitions of the eligible task types' rather than 'stability across different held-out task distributions.' We will explicitly note the overlap in task types across splits. Regarding option (b), constructing a fully disjoint split from the 10 eligible task types is feasible in principle (e.g., two disjoint sets of 5), but we are constrained by the fact that only 10 task types are eligible and we use 5 for testing in each split, leaving limited room for fully disjoint configurations. We will note this constraint and frame it as future work once r_C is extended to handle multi-step collisions, which would expand the eligible pool. revision: yes

  3. Referee: The symbolic extraction functions phi(r) and psi(o)/psi(v) are described as general but instantiated only for PHYRE (3x3 spatial grid, first-collision extraction). The cross-environment transfer to Virtual Tool and CRAFT VQA presumably reuse the same phi/psi, but the paper does not describe how these functions are adapted for environments with different object types, action spaces, or physics. The paper should clarify whether phi/psi are reused as-is or adapted, and what the adaptation entails.

    Authors: The referee is correct that the manuscript does not adequately explain how phi/psi are handled for the Virtual Tool and CRAFT VQA experiments. We will clarify this in the revised manuscript. The key distinction is between training and evaluation: (1) For cross-environment transfer to Virtual Tool (Table 2) and CRAFT VQA (Table 3), the model is trained on all 25 PHYRE task types using the PHYRE-instantiated phi/psi. At evaluation time on Virtual Tool and CRAFT VQA, the same phi/psi are reused as-is — the model is prompted to produce reasoning traces in the same structured format (scene_answer, causal_actions_answer, placement_answer), and the symbolic extraction functions parse these traces identically. No environment-specific adaptation of phi/psi is performed. (2) For Virtual Tool, the prompt is adapted to reflect the different object types and shapes (e.g., trapezoids, triangles) and the different goal condition (green touches blue, without the purple static-object variant), but the symbolic extraction logic — 3x3 spatial grid, collision event tuples, placement descriptions — remains unchanged. (3) For CRAFT VQA, the model produces scene_answer and causal_actions_answer but no placement_answer (since no action is taken), and the final_answer tag is added for the VQA response. The phi/psi functions parse the same symbolic tuples. (4) Importantly, during evaluation on Virtual Tool and CRAFT VQA, the VAORA rewards (r_G, r_P, r_C) are not computed — these are training-time rewards that require simulator access. The evaluation is purely inference-based: the model generates reasoning traces and actions/answers, and we measure task success (Virtual Tool) or answer accuracy (CRAFT VQA). So the cross-environment claim is specifically that the reasoning structure learned via VA revision: no

Circularity Check

0 steps flagged

No significant circularity: VAORA's reward components are defined against simulator-extracted ground truth, and the DQN-as-baseline concern is mitigated by ablation showing VAORA rewards add independent signal.

full rationale

The paper's central claim is that VAORA's dual reward design enables cross-task generalization surpassing the DQN expert. Walking the derivation chain: (1) The Visual Alignment Reward r_G (Eq. 3) is defined by comparing the VLM's parsed symbolic propositions phi(r) against ground-truth states psi(o) extracted directly from the simulator — not against the DQN's outputs. (2) The collision reward r_C (Eq. 4) and placement reward r_P (Eq. 5) similarly compare phi(r) against psi(v) extracted from post-interaction simulation outcomes. These are independently grounded in simulator state, not circularly defined. (3) The DQN expert's success probability r_DQN (Eq. 6) is used as a gating signal for the visual-action alignment reward and as a training stabilizer, and the DQN is also used as a baseline for comparison. This creates a potential circularity concern (fitted input called prediction): the VLM is trained with a reward gated by DQN estimates and then compared against the DQN itself. However, this is NOT circular by construction because: (a) the DQN provides a smooth success-probability estimate, not the exact binary task-success signal — the VLM's final evaluation metric (Pass@k) is the environment's binary success, which is independent of the DQN; (b) the ablation in Table 5 shows +EG alone (DQN reward without VAORA) underperforms +EG+VAORA, demonstrating that VAORA's alignment rewards provide signal beyond what the DQN gating alone provides; (c) the DQN is trained only on training-set tasks and evaluated on held-out tasks it was never trained on, so the comparison is not on the DQN's home turf. The SFT dataset (Appendix B.1) is constructed using Gemini-3.1-Flash to generate reasoning traces conditioned on simulation-extracted information, but this is used only for the SFT warm-start, not for the RL reward. No self-citation chain is load-bearing: the paper cites GDPO [30] as an optimization method (external, not authored by present authors) and the DQN training follows the original PHYRE paper [8] (external). The phi/psi extraction functions are domain-specific transformations defined in the paper itself (Sec. 3.2) and are not imported from prior work by the same authors. The restriction of test tasks to 10/25 types where first-collision is the key interaction (Appendix B.2) is a scope limitation, not a circularity — the reward is not defined in terms of the evaluation outcome. Overall, the derivation is self-contained against external benchmarks, and the DQ

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 0 invented entities

The paper introduces no new physical entities, particles, or forces. The 'invented' components are reward functions and symbolic extraction procedures, which are methodological contributions rather than postulated entities. The DQN expert is a standard pre-trained agent, not a new architectural entity. The main audit concern is the hand-set reward weights and the domain assumption restricting evaluation to first-collision tasks.

free parameters (5)
  • wg (grounding reward weight) = 0.2
    Hand-set weight for the grounding reward component, reflecting relative importance of scene perception alignment.
  • wc (collision reward weight) = 0.6
    Hand-set weight for the collision reward component, set highest to emphasize causal interaction prediction.
  • wp (placement reward weight) = 0.3
    Hand-set weight for the placement reward component, reflecting importance of action-reasoning alignment.
  • pg, pc, pp (penalty coefficients) = 1/2, 1/6, 1/3
    Derived from constraint w·p=0.1 to ensure uniform -0.1 penalty for formatting errors across reward types.
  • DQN expert architecture and training hyperparameters = Not fully specified
    The DQN expert is trained following the original PHYRE paper procedure but specific hyperparameters are not enumerated, introducing unstated configuration choices.
axioms (4)
  • domain assumption The first collision event triggered by the placed ball is a sufficient proxy for the full physical interaction chain.
    Appendix B.2: The collision reward r_C is designed to model only the first object the red ball collides with. This restricts evaluation to 10 of 25 task types where the key interaction is the first collision, assuming this captures the essential physics.
  • domain assumption Structured symbolic propositions extracted via prompt engineering from VLM output faithfully represent the model's internal reasoning.
    Section 3.2: The method relies on φ(r) parsing structured tuples from reasoning traces within XML tags. This assumes the model's symbolic output reflects its actual reasoning process rather than learned template matching.
  • domain assumption A 3×3 spatial grid provides sufficient resolution for grounding and placement rewards.
    Section 3.2 and Appendix B.4: The soft-grid score maps spatial labels to a 3×3 grid, assuming nine regions provide adequate granularity for physical reasoning alignment.
  • standard math GDPO (Group reward-decoupled normalization policy optimization) is a suitable optimization framework for multi-reward VLM training.
    Section 3.3: The final reward is optimized using GDPO [30], which is cited as an established method for multi-reward RL optimization.

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Reference graph

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    Output the final concise answer . OUTPUT FORMAT ( MUST MATCH EXACTLY ) You MUST output EXACTLY these se ct ion s in EXACT order , and NOTHING else : < scene_reasoning > - I de nti fy all re lev an t objects and e st im ate their a p p r o x i m a t e p o s i t i o n s . - Analyze spatial r e l a t i o n s h i p s : alignment , distance , and lo ca ti on r...

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