Video models answer from sampled frames and may miss the decisive moment. A common remedy is to commit to an answer, re-examine the video, and confirm or revise it, a loop now mainly learned with reinforcement learning (RL). We find that RL erodes the initial answer, a phenomenon we term confidence erosion, where confidence means the label-verified reliability of the initial answer instead of self-reported certainty: because errors can be repaired later, a correct initial answer earns less reward, and the initial answer stalls even as the final answer's gain over it rises. We propose the principle confident first, reflective second: the initial answer must stay reliable on its own, above a floor set by a reference policy, and reflection may only add to it. Our method, CoFire, implements this principle with one mechanism for each erosion channel: reward incentives, parameter drift, and gradient interference. CoFire reaches 67.0% mean accuracy on Video-MME, MLVU, and LongVideoBench, 1.7 points above the strongest compared 7B method, and improves initial-answer and final accuracy over outcome-reward RL by 4.1 and 3.7 points on average: a stronger initial answer carries through to a stronger final one.
Figures & tables
Figure 1: Reflection gain is not reflective progress. Video-MME policies by initial-answer accuracy and reflection gain Δ ; dashed lines are iso-lines of final accuracy. From the same SFT policy, Outcome RL moves up (more gain, same initial answer), while CoFiRe moves right to a higher final accuracy. Gray dots: configurations from Table 8 .
Figure 2: Answering and re-examining a Video-MME example. From a 2 fps preview and an eight-frame first look showing only two mugshots, Spre answers y0= C. Sgate reports low confidence and chooses Seek ; frames at 258–261 s reveal a third mugshot, and Srev revises to yf= D, a fix . More examples are in Appendix G .
Figure 3: Overview of CoFiRe . Top: G rollouts of one policy answer ( Spre ), choose how to check ( Sgate ), and confirm or revise ( Srev ). Middle: stage-routed advantages penalize the initial answer of an err-then-fix rollout but credit the repair. Bottom: one mechanism per erosion channel: λ rises below the initial-answer accuracy floor (incentive), verified reference answers anchor the decision token (drift), and only the conflicting part of the reflection gradient is removed (interference).
Figure 4: Penalizing revision does not protect the initial answer. Video-MME Seek rate, the share of questions routed to Seek (bars, left) and final accuracy (line, right), varying one penalty from the default β=1.5 , γ=0.2 .
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Fix: the gate catches a misread first-look frame (Video-MME 180-3, 66 s video). Spre reports KOR from a scoreboard that lists CHN first; the gate re-reads it, and dense retrieval around the finish confirms the revision to C.
Figure 6: Fix: localizing a few seconds of evidence in a long video (Video-MME 766-1, 19 min). Spre guesses the athlete from the uniform color; the gate requests the bib, and all retrieved frames fall in the treatment segment, where the bib reads 377.
Figure 7: Fix: replacing a language prior with on-screen text (Video-MME 713-3, 42 min). Spre answers from the documentary title; retrieval returns the full headline attributing the threat to Dutch drug gangs.
Figure 8: Failure: a correct initial answer broken by a salient but irrelevant number (Video-MME 568-1, 13 min). Spre is correct but not grounded in an on-screen figure; the query retrieves a 1916 rail-network statistic that matches option A verbatim, and the revision attributes it to the traveler.