Paper Citation Record · LEDGER

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

As of 6 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 50 inbound Pith citation observations for arXiv:2504.11468.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.11468 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-17T15:43:34.652373Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 50 of 50 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T23:03:06.609642Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy12
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 377d1c5d-6337-4d2c-965b-2bcd861d1e8c · outbound

This paper cites description.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models description

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.680977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2e6fdd68-68aa-4523-b52e-b9daa09501fe · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.688049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6c138ff6-9497-47a2-bd75-c1f2e83be658 · outbound

This paper cites —— Here is the input: {input} Figure 10: Prompt for answer rewriting with GPT-4-Turbo.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models —— Here is the input: {input} Figure 10: Prompt for answer rewriting with GPT-4-Turbo

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.693175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d8b0a96e-1bc8-42c0-afdd-a53cf6be75dc · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.696741Z

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Observation 1f915c4d-46b9-46a5-a615-e9b11f715519 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.700302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c214b2cc-f479-42a5-ba73-a1a99e336103 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.703720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bf48c659-72e1-44bd-9221-f4de2d795301 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.707323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f9d54b84-939a-432b-9eef-6004030516f8 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.710824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 817710e1-b9a3-4930-9f21-4c10b1b614da · outbound

This paper cites A”, “A)”, “(a).

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models A”, “A)”, “(a)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.714333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fd176a77-ae3b-455e-aec0-13f3cc800221 · outbound

This paper cites - The angle ∠ AOB = 36∘.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models - The angle ∠ AOB = 36∘

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.717727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0bb6b597-dbce-475a-ac09-8ec07744f140 · outbound

This paper cites Therefore, ∠ OBA = 90.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Therefore, ∠ OBA = 90

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.721190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f9130c08-2e24-4054-8414-3bfa1debef23 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.724942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a00ecbf7-cba9-4c20-b997-cfecd68a0b6f · outbound

This paper cites - Points C and D are on the semicircle, with D being the midpoint of arc BC.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models - Points C and D are on the semicircle, with D being the midpoint of arc BC

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.728598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 11eb9db7-a71f-474e-a285-ef97a3632064 · outbound

This paper cites - Midpoint of Arc: Since D is the midpoint of arc BC, arcs BD and DC are equal.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models - Midpoint of Arc: Since D is the midpoint of arc BC, arcs BD and DC are equal

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.733236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4ad2767c-783d-479e-b817-83c8d5a89262 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.736435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a4812136-1b4d-4a3f-b69a-d0d469bfa25a · outbound

This paper cites Let each be.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Let each be

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.739290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3fd4b36e-47d7-4f48-bc20-062b7fafde3f · outbound

This paper cites - By the Inscribed Angle Theorem.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models - By the Inscribed Angle Theorem

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.742450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9381ce95-e650-48ae-a8af-c2c11af64fc2 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.745099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f3ab0f9d-be62-43d5-895c-ff53adfe68b4 · outbound

This paper cites How many objects are left? •Original Answer: 3 Input Image <think> Okay, let's see.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models How many objects are left? •Original Answer: 3 Input Image <think> Okay, let's see

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.748216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7dc0860c-e552-4caf-88ab-d4217fe9ea96 · outbound

This paper cites To find the value of , I'll substitute the coordinates of the point into the equation.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models To find the value of , I'll substitute the coordinates of the point into the equation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.751072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6d55bae2-aee4-497b-a2dd-16e25d5c1b82 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.753665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8f991278-9cf0-436c-8ba8-c5ec09e9d91b · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.756381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e8ab3baf-dc7d-47a8-85f8-4e8f74c3061a · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.758933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 585d2d22-91e7-483f-91d3-8f3dc50cc1d5 · outbound

This paper cites an unresolved cited work.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:43:34.761573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 649384af-e3c0-4f3c-9363-7e67250ae337 · outbound

This paper cites How many objects are left? •Original Answer: 3 Input Image <think> Okay, let's see.

SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models How many objects are left? •Original Answer: 3 Input Image <think> Okay, let's see

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:43:34.764485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

Observation 663300b5-aaeb-4403-ae12-0634e5dbdc59 · inbound

OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles cites this paper.

OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-19T06:59:03.351961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6388e0e0-b733-4138-9ed3-b930cdf92b31 · inbound

GRIT: Teaching MLLMs to Think with Images cites this paper.

GRIT: Teaching MLLMs to Think with Images SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 34

Resolution: metadata mismatch; local_arxiv, observed 2026-05-22T13:31:36.123124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation edeabbe8-7b09-4e80-bc84-dfeaafec1ec7 · inbound

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling cites this paper.

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:40:46.153525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ced92bbf-66a5-4550-aaa8-51ebd37cd72b · inbound

WebSailor: Navigating Super-human Reasoning for Web Agent cites this paper.

WebSailor: Navigating Super-human Reasoning for Web Agent SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 21777939-f9bb-4490-ade6-fcc4ea013ce3 · inbound

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 240

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 52ab568b-7125-4c5f-8753-0f7549bbe07d · inbound

MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math Reasoning in Multimodal Large Language Models cites this paper.

MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math Reasoning in Multimodal Large Language Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T23:03:06.609642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 584c915c-2f4b-4f5e-abde-3e2cf2752b2d · inbound

UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning cites this paper.

UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-18T22:21:53.234074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4f8c97d2-a1cf-4e78-8df9-dba7bdfd113c · inbound

Reinforced Visual Perception with Tools cites this paper.

Reinforced Visual Perception with Tools SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:04.885212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd903724-f451-48e7-ab85-592fedc27b69 · inbound

Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation cites this paper.

Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T05:34:40.243499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93596ae4-2186-456b-8029-e254c01e1a43 · inbound

Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding cites this paper.

Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T22:57:44.986207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe009cb9-2b50-4598-83d3-acbabef4f6b3 · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T16:07:28.361696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1d1bde5-9bfa-410f-af95-ce1fe03e63c7 · inbound

Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions cites this paper.

Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-18T15:01:31.296494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d4b59fc0-9878-4ddd-876b-91f6ccbf08e0 · inbound

Mixture-of-Visual-Thoughts: Exploring Context-Adaptive Reasoning Mode Selection for General Visual Reasoning cites this paper.

Mixture-of-Visual-Thoughts: Exploring Context-Adaptive Reasoning Mode Selection for General Visual Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:36:25.155140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9b8dd9e8-3217-48f5-9044-2713a86776b4 · inbound

Dynamic-TreeRPO: Breaking the Independent Trajectory Bottleneck with Structured Sampling cites this paper.

Dynamic-TreeRPO: Breaking the Independent Trajectory Bottleneck with Structured Sampling SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-21T21:30:39.192536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2835058f-2941-4d99-9c7d-e98982c5b5c9 · inbound

Latent Visual Reasoning cites this paper.

Latent Visual Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8c171be8-466d-4c50-a6b1-6e4562419737 · inbound

VOLD: Reasoning Transfer from LLMs to Vision-Language Models via On-Policy Distillation cites this paper.

VOLD: Reasoning Transfer from LLMs to Vision-Language Models via On-Policy Distillation SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T07:57:22.810986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94162f6f-42a4-47c7-84a6-2e99cc85a321 · inbound

DeepEyesV2: Toward Agentic Multimodal Model cites this paper.

DeepEyesV2: Toward Agentic Multimodal Model SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 227654e6-ca34-4b57-9ab6-70a07116a4b7 · inbound

Reinforcement Learning Improves Traversal of Parametric Knowledge in LLMs cites this paper.

Reinforcement Learning Improves Traversal of Parametric Knowledge in LLMs SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:34.982009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c20dbad-0a8a-46b7-bf7b-cb81c16b9fea · inbound

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models cites this paper.

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T21:09:13.251555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f0c7d33-ee0b-4908-8407-12343980bcf5 · inbound

TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots cites this paper.

TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:30:11.116491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ddf35aeb-ab0b-4711-8dff-c7247bb83273 · inbound

Distilling Counterfactual Reasoning from Language to Vision: Causal Graph Guided Post-Training for Video Understanding cites this paper.

Distilling Counterfactual Reasoning from Language to Vision: Causal Graph Guided Post-Training for Video Understanding SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T20:27:11.411364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feca519a-fadb-41ef-bbfc-b48c1c8ccc14 · inbound

Asking like Socrates: Socrates helps VLMs understand remote sensing images cites this paper.

Asking like Socrates: Socrates helps VLMs understand remote sensing images SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 88674871-a74a-4b1b-8776-512f7945ef01 · inbound

Reasoning Within the Mind: Dynamic Multimodal Interleaving in Latent Space cites this paper.

Reasoning Within the Mind: Dynamic Multimodal Interleaving in Latent Space SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9cf111ae-be03-4f32-b561-3108f6f4307d · inbound

Mimic Human Cognition, Master Multi-Image Reasoning: A Meta-Action Framework for Enhanced Visual Understanding cites this paper.

Mimic Human Cognition, Master Multi-Image Reasoning: A Meta-Action Framework for Enhanced Visual Understanding SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T11:13:16.762815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 823cb637-64b5-4a87-90d6-d521c9eadfcb · inbound

ReMoT: Reinforcement Learning with Motion Contrast Triplets cites this paper.

ReMoT: Reinforcement Learning with Motion Contrast Triplets SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T19:58:14.742025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c70ed5cd-9388-4a7e-92ce-e6a59593d17f · inbound

Teaching an Agent to Sketch One Part at a Time cites this paper.

Teaching an Agent to Sketch One Part at a Time SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6e169cf3-e0d2-41a3-b71a-0e8a488fd26a · inbound

Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models cites this paper.

Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9b8e4736-2200-4c0d-8ff0-d93c1f1727df · inbound

Watch Before You Answer: Learning from Visually Grounded Post-Training cites this paper.

Watch Before You Answer: Learning from Visually Grounded Post-Training SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 53641dc4-bc0b-471e-96dc-847ae3396003 · inbound

Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models cites this paper.

Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 63c54e0e-f8e2-44a0-8c55-0f24920ef4a1 · inbound

Generalization in LLM Problem Solving: The Case of the Shortest Path cites this paper.

Generalization in LLM Problem Solving: The Case of the Shortest Path SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7c852431-d69f-4047-8c7c-257aa0598ff3 · inbound

Characterizing Model-Native Skills cites this paper.

Characterizing Model-Native Skills SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 69ca1e44-4ed7-4c75-96e5-13fe8d1825ff · inbound

One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models cites this paper.

One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 144

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b1f6c7e3-8b8c-4ebb-a27d-2f6cb7d31507 · inbound

CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding cites this paper.

CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e8051922-815f-4367-8c4b-300a29563b98 · inbound

How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors cites this paper.

How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f2176436-10eb-4ea0-bbf8-da2b115a9287 · inbound

Learning Agentic Policy from Action Guidance cites this paper.

Learning Agentic Policy from Action Guidance SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 11233d49-b0da-4cfd-9cd2-a9385544f928 · inbound

Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence cites this paper.

Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b98e07f9-c4ac-4ba1-9170-79703cd4b01f · inbound

Are Tools Always Beneficial? Learning to Invoke Tools Adaptively for Dual-Mode Multimodal LLM Reasoning cites this paper.

Are Tools Always Beneficial? Learning to Invoke Tools Adaptively for Dual-Mode Multimodal LLM Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 40

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T06:18:05.178174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9266a140-c721-4919-ad30-99c840ebd9e8 · inbound

RISE: Reliable Improvement in Self-Evolving Vision-Language Models cites this paper.

RISE: Reliable Improvement in Self-Evolving Vision-Language Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-21T05:39:40.523567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b0cb786d-7ac9-4b0e-8b91-a44e05412304 · inbound

RISE: Reliable Improvement in Self-Evolving Vision-Language Models cites this paper.

RISE: Reliable Improvement in Self-Evolving Vision-Language Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-06-30T17:24:57.491852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3785af52-345f-441d-a180-2d76d5c2776b · inbound

AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution cites this paper.

AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-06-30T00:14:04.569192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 30f6306f-c6fc-4380-a466-508f6fcaa267 · inbound

ToolFG: Towards Well-Grounded Fine-Grained Image Classification cites this paper.

ToolFG: Towards Well-Grounded Fine-Grained Image Classification SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-01T22:56:20.349143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a4d67d8c-9984-4a16-86f5-a83c116e52e9 · inbound

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients cites this paper.

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:56.121032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1826d3fd-d7dc-49db-a281-4d2ef82897ab · inbound

Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning cites this paper.

Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-04T16:29:57.013212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6c48bdaf-3f3a-4b4a-b366-ee810459815d · inbound

Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment cites this paper.

Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-08T03:14:31.698320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 757dbb39-9cbf-49c1-b8e5-010dd6e76ef0 · inbound

BUS: Brain-Inspired Unsupervised Self-Reflection via Backward Prediction for Multimodal Reasoning cites this paper.

BUS: Brain-Inspired Unsupervised Self-Reflection via Backward Prediction for Multimodal Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 110

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T13:56:19.219331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ac7f4469-3187-48a0-ae8c-a5b8904426d0 · inbound

BUS: Brain-Inspired Unsupervised Self-Reflection via Backward Prediction for Multimodal Reasoning cites this paper.

BUS: Brain-Inspired Unsupervised Self-Reflection via Backward Prediction for Multimodal Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 110

Resolution: unresolved; no resolver link, observed 2026-07-13T06:45:27.857034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b2f3b5e-95e9-43e5-89b4-e24edf9eb807 · inbound

SIVA-RL: Sensitivity-Invariance Visual Alignment for Multimodal Reinforcement Learning cites this paper.

SIVA-RL: Sensitivity-Invariance Visual Alignment for Multimodal Reinforcement Learning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T03:20:41.224809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69641fed-a3c3-4135-b179-c2f374505a6a · inbound

Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding cites this paper.

Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-07-31T21:13:05.201592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf4436d1-3642-4344-af48-54e7b424dc8c · inbound

Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning cites this paper.

Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T11:36:49.162119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 701a8f4f-69ed-42a1-9375-ccd59810fc97 · inbound

Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs cites this paper.

Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T19:03:07.091870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.