ARES: Enhanced Multi-Modal CoT Reasoning

Updated 9 November 2025

The paper introduces a dual-stage training loop combining reinforcement learning and supervised fine-tuning to improve rationale coherence and problem-solving accuracy.
It leverages self-consistency sampling, tree search methods, and entropy-driven adaptive policies to optimize multi-modal reasoning and adjust chain lengths based on task difficulty.
Empirical results demonstrate significant improvements in answer accuracy and rationale quality, while highlighting challenges with token costs, complexity, and deployment trade-offs.

ARES: Enhanced Multi-Modal Chain-of-Thought Reasoning encompasses a set of techniques and frameworks designed to improve the reasoning abilities of large multimodal language or vision-LLMs (MLRMs, LVLMs) by tightly integrating visual and textual modalities within chain-of-thought (CoT) prompting and decoding. The scope of “ARES”-style systems now includes a diverse landscape: alternating reinforcement learning and fine-tuning with detailed AI feedback, inference-time scaling via self-consistency and tree search over multimodal reasoning chains, rationale/entropy-aware adaptive policies, and difficulty-adaptive exploration. Core achievements include significant improvements in answer accuracy, efficiency, and rationale quality on challenging multi-modal tasks, while posing new questions around cost, grounding, and deployment.

The “ARES” designation covers multiple complementary algorithmic paradigms unified by the goal of explicitly leveraging both visual and textual information for multi-step reasoning:

Alternating Reinforcement Learning and Supervised Fine-Tuning

ARES (Byun et al., 25 Jun 2024) adopts a two-stage training loop:

Reinforcement Learning (RL) Stage: An advanced AI feedback provider (Teacher; e.g., GPT-4, Claude 3) scores each sentence $s_i$ in a generated CoT $\tau$ for its problem-solving utility $R(s_i)\in[0,1]$ . Scores are centered and used as sentence-level rewards in PPO, with actions corresponding to sentences:

$r_i = R(s_i)-0.5,\quad R_\text{total}(\tau) = \sum_{i=0}^k \gamma^i r_i$

Supervised Fine-Tuning (SFT) Stage: The RL-fine-tuned model's CoTs are sent back to the Teacher for high-fidelity correction, creating a corpus for further cross-entropy fine-tuning.

The alternation yields more valuable and coherent rationales while addressing RL-induced degeneration (e.g., repetition, truncation).

Inference-time scaling (Lin et al., 17 Feb 2025) involves sampling and search methods over reasoning traces:

Sampling-Based (Self-Consistency): Multiple multimodal CoT traces $\tau^{(i)}$ are sampled independently via

$\tau^{(i)}\sim p(\tau\mid x;T),\ y^{(i)} = \mathrm{Answer}(\tau^{(i)})$

Aggregation is typically through majority vote or via a consistency-enhanced verifier.

Tree Search–Based (e.g., MCTS): A search tree explores possible reasoning states, using a consistency verifier to score nodes and guide expansion. The search heuristics blend textual and visual consistency terms:

$V(\tau) = \alpha S_\text{text}(\tau) + \beta S_\text{vis}(\tau)$

with $S_\text{text}$ (log-probability), $S_\text{vis}$ (cross-modal embedding alignment), and cross-trace agreement.

Entropy-Driven Adaptive Reasoning and Policy Optimization

A recent “ARES” framework (Chen et al., 9 Oct 2025) implements difficulty-adaptive CoT length and exploration strategies using token-level entropy:

Adaptive Cold-Start: Models are supervised on traces of length matched to problem difficulty.
Adaptive Entropy Policy Optimization (AEPO): Online, window-averaged entropy (HWE) triggers exploration at reasoning-critical junctures:

$HWE_t = \frac{1}{w} \sum_{i=t-w+1}^t H_i$

Combined with hierarchical entropy-shaped rewards and dynamic KL control, AEPO encourages brevity on easy cases and depth on hard ones.

2. Scoring, Verifier Integration, and Reward Structuring

Central to ARES methodologies is the use of detailed verifier and reward models for both training and inference:

Consistency-Enhanced Verifier

For self-consistency and tree search (Lin et al., 17 Feb 2025), the verifier fuses:

Textual plausibility: Sum of log-probabilities across textual steps.
Visual alignment: Negative visual-semantic distance between predicted and ground-truth image regions, typically in a CLIP- or ViT-derived joint embedding space.
Cross-trace agreement: Fractional agreement among independently sampled final answers.

Sentence-Level AI Feedback

ARES (Alternating RL–SFT) (Byun et al., 25 Jun 2024) leverages sentence-wise feedback from frontier LLMs, enabling more granular reward shaping compared to summary-level ranking. Teachers are prompted to score sentences for informativeness toward problem-solving, supporting fine-grained PPO reward allocation.

Entropy and Difficulty-Aware Shaping

Difficulty estimation via pass@N under a strong solver enables bucketing of problems into easy/medium/hard, each with matched CoT lengths and difficulty-curated exploration rewards (Chen et al., 9 Oct 2025). Hierarchical shaping functions $g_d(\Delta)$ penalize over-exploration on easy items, under-exploration on hard, and symmetrically for medium.

3. Empirical Results and Quantitative Gains

The multi-modal ARES family demonstrates substantial improvements across a range of publicly benchmarked multimodal and STEM reasoning datasets:

Model	Task/Dataset	Rationale Quality (GPT-4o Win Rate)	Answer Accuracy (%)	CoT Efficiency Impact	Notes
ARES (Alternating RL-SFT; (Byun et al., 25 Jun 2024))	ScienceQA	69.8 (Base), 73.8 (Large)	88.38 (+2.79), 91.09 (+0.83)	–	Baseline MM-CoT: 85.95, 90.26
	A-OKVQA	69.1 (Base), 67.0 (Large)	65.41 (+4.45), 68.03 (+2.35)	–	Baseline MM-CoT: 60.96, 65.68
Inference-Scaling (Lin et al., 17 Feb 2025)	10-domain (avg)	–	Text-only CoT: 62.0; Pure Multi-modal: 68.0; Self-consistency: 72.0; Tree search: 74.0; Blended: 75.0	Multi-modal increases tokens by ~53%, Sampling ×10, Tree search ×20	Sampling ≈2× cheaper than Tree search
ARES-7B (Entropy-adaptive; (Chen et al., 9 Oct 2025))	10 Multimodal BMs	–	55.9 (+9.7pp over best open-source baseline, Vision-G1)	CoT length –15-20% on easy, +10-15% on hard; total cost –25%	GPT-4.1 at 61.8%
	6 Textual BMs	–	59.6 (+28.6pp vs. MM-Eureka-Qwen-7B)	–	Outperforms GPT-4.1: 56.7%

Across frameworks, the main sources of improvement include both accuracy and quality of natural language rationales, particularly on reasoning-intensive visual tasks. Blended sampling and adaptive policies yield additional diversity, while simultaneously compressing easy-case traces.

4. System Architectures and Implementation Considerations

Various ARES instantiations have adopted distinct architectural approaches while converging on end-to-end, unified Backbones:

Backbone Models: Typically T5- or Alpaca-style transformers fused with ViTs for visual encoding (Byun et al., 25 Jun 2024, Lin et al., 17 Feb 2025), sometimes using lightweight adapters (LoRA).
Specialized Adapters: LoRA adapters for answer inference from CoTs, trained as small parameter overheads (Byun et al., 25 Jun 2024).
Verifier/Teacher Model Integration: External calls to strong LLMs (GPT-4, Claude 3 Haiku) for supervision and correction, or bespoke verifier modules fusing vision and language.

Computational Requirements:

Sampling and tree search scale memory and token usage linearly or worse with exploration budget. E.g. tree search with $B=50$ costs $\sim4000$ tokens/sample and incurs up to $\sim$ 40GB GPU scratch for LVLM state caching (Lin et al., 17 Feb 2025).
Adaptive policies (Chen et al., 9 Oct 2025) compress easy-case CoTs, reducing total inference cost by $\sim$ 25% relative to fixed-length non-adaptive RL, with additional compute cost for reward shaping and KL budgets.

Deployment Trade-offs:

Sampling-based methods are amenable to parallel execution, while tree search is memory- and latency-bound due to sequential rollouts and verifier scoring (Lin et al., 17 Feb 2025).
RL-based ARES alternation requires additional engineering for Teacher API latency, but stabilization via SFT can reduce post-deployment error rates (Byun et al., 25 Jun 2024).

5. Limitations, Open Challenges, and Future Directions

Token Cost and Visual Reasoning Overhead

The token and compute costs of multi-modal CoT reasoning—driven by the need to encode visual steps as tokens—are nontrivial. For pure multi-modal CoT, token usage rises $\sim$ 53% over text-only baselines; with N=10 samples, this multiplies further (Lin et al., 17 Feb 2025). Large-scale deployment thus requires visual token compression or adaptive cropping to reduce bloat.

Grounding, Hallucination, and Faithfulness

Despite improved accuracy, some regimes (e.g., open-ended visual commonsense) show persistent high variance and hallucination risk; hybrid scoring, better joint vision-language verifiers, and rationale-conditioned decoders are active research areas.

Adaptivity and Difficulty Estimation

ARES-style approaches depend on accurate difficulty estimation (often via pass@N against strong solvers) to bucket problems and allocate reasoning effort (Chen et al., 9 Oct 2025). Self-supervised curriculum strategies or richer difficulty models are plausible future enhancements.

Current methods are largely focused on vision-text tasks. Extending to audio, video, or interactive environments remains an open technical challenge; generalized search/branching and hybrid inference paradigms (e.g., adaptive switch between sampling and tree search) have been highlighted as promising research avenues (Lin et al., 17 Feb 2025).

ARES-style enhanced multi-modal CoT reasoning is part of a broader evolution of modular and adaptive reasoning systems:

CMMCoT (Zhang et al., 7 Mar 2025): Grounds interleaved slow-thinking chains in visual region tokens and augments with test-time memory over multiple images.
Rationale-Enhanced Decoding (RED) (Yamaguchi et al., 10 Jul 2025): Theoretically justified fusion of image- and rationale-conditional distributions at decoding, yielding improved grounding and faithfulness—especially prominent in mitigating rationale/vision disconnects observed in vanilla CoT prompting.

A convergent trend is the increasing sophistication of CoT supervision, verifier integration, and adaptive exploration strategies, with empirical evidence that these methods not only improve headline accuracy but also the interpretability, compactness, and coherence of model-generated reasoning.

Enhanced multi-modal CoT frameworks under the ARES rubric have demonstrated that tightly coupled, verifier-guided, and difficulty-adaptive reasoning models can set new standards for both accuracy and efficiency in multi-modal reasoning, but realize these gains by incurring additional implementation, cost, and engineering complexity. There remain significant opportunities to further improve grounding, reduce overhead, and generalize this paradigm across broader domains and modalities.