Solvent: Long-Horizon Reasoning for Multi-Step Retrosynthesis

Yifei Wang; Botian Wang; YiFei Wang; Jiangtao Feng; Xinbo Zhang; Yawen Ouyang; Yixin Wu; Lihao Wang

cs.AIOct 23, 2026

Solvent: Long-Horizon Reasoning for Multi-Step Retrosynthesis

Authors: Yifei Wang, Botian Wang, YiFei Wang, Jiangtao Feng, Xinbo Zhang, Yawen Ouyang, Yixin Wu, Lihao Wang,

Organizations: Fudan University · Shanghai Artificial Intelligence Laboratory · Generative Symbolic Intelligence Lab (GenSI), Tsinghua University

Abstract

Multi-step retrosynthesis requires recursively transforming a target molecule through successive reaction steps into purchasable building blocks, and recent work has begun to draw on large language model (LLM) reasoning to tackle the task. In its latest evolution, LLM reasoning has entered the long-horizon regime: models pursue objectives across interdependent steps, interact with environments, and adapt to feedback. This shift has markedly advanced LLM capabilities, as illustrated by breakthroughs in AI-driven mathematical research. At this frontier, we introduce Solvent, an 8B LLM that successfully extends long-horizon reasoning to multi-step retrosynthesis. Through a two-round post-training curriculum, Solvent learns to interleave iterative synthesis planning, external availability verification, and adaptive route revision. Experiments show that, following a $2.1\times$ performance boost from RL, Solvent scores 61.86% across four benchmarks, surpassing mid-scale language models and prior search-based retrosynthesis systems, and rivaling frontier trillion-parameter LLMs.

Figures & tables

Figure 1: At 8B parameters, Solvent substantially outperforms mid-scale LLMs and rivals frontier trillion-parameter models. Proprietary models with undisclosed parameter counts are arbitrarily positioned in the shaded region.

Figure 2: Overview of Solvent. Left: Two rounds of post-training prepare Solvent for effective RL. Round 1 develops a seed policy with retrosynthetic knowledge and stable reasoning. Round 2 bootstraps a stronger starting policy using the seed policy, enabling final-stage RL to substantially boost performance. Right: Solvent performs long-horizon reasoning through iterative planning, external verification, and adaptive revision.

Method	Params.	Pistachio Hard	Pistachio Reachable	USPTO-190	ChEMBL- 1000	Average (%)
Frontier LLMs (proprietary)
Claude Opus 4.8 †	—	38.57	77.50	54.44	55.63	56.54
Gemini 3.5 Flash †	—	42.86	72.50	52.22	65.63	58.30
Grok 4.5 †	—	52.86	71.25	50.00	62.50	59.15
GPT-5.5 †	—	58.57	82.50	54.44	58.75	63.57
Frontier LLMs (open-weight)

Table 1: At 8B parameters, Solvent remains competitive with frontier LLMs across four multi-step retrosynthesis benchmarks. We report parameter counts, benchmark-specific success rates, and their average. To enable cost-controlled, target-matched evaluation, selected models are evaluated on a shared 400-target subset and marked with

\dagger

. Em dashes denote undisclosed parameter counts. Gray shading and boldface mark the best frontier and mid-scale results, respectively.

Performance on USPTO-190
Method	$N=50$	$N=100$	$N=200$	$N=300$	$N=400$	$N=500$
Greedy DFS	19.47	19.47	19.47	19.47	19.47	19.47
retro*	38.95	50.00	68.95	74.74	77.89	79.47
retro*-0	27.37	37.89	55.79	62.63	72.11	74.21
retro*+	55.79	68.42	79.47	83.16	84.21	84.21
retro*+-0	47.89	61.05	76.84	82.11	85.79	86.32

Table 2: Comparison with prior search-based retrosynthesis systems across search budgets. Here,

N

tracks how much explicit reaction-level construction each method performs before finding a successful route, with the cross-method accounting defined in Appendix C.2 . Bold and underlined values denote the best and second-best results, respectively.

Training stage		Pistachio Hard	Pistachio Reachable	USPTO- 190	ChEMBL- 1000	Average
Initial Backbone		0.00	0.00	0.53	0.20	0.18
Round 1	Teacher-guided cold start	2.00	12.67	3.16	7.20	6.26
	First-round RL	19.00	40.00	9.47	22.80	22.82
Round 2	Reasoning bootstrapping	23.00	41.33	22.63	29.80	29.19
	Final-stage RL	62.00	78.00	48.95	58.50	61.86

Table 3: Stage-wise performance across the two-round post-training curriculum. We report performance for four training stages described in Sec. 4.1 and Sec. 4.2 . Average success improves across the reported stages, with the largest gain occurring after final-stage RL.

Figure 3: The benefit of additional interaction turns increases throughout post-training. (a) Average success rate under turn limits of 2–5. (b) Success gain per additional turn across stages.

Figure 4: Final-stage RL successively improves chemical validity and stock completion. (a) Performance on validation set during final-stage RL. (b) Evolution of chemical validity and stock completion. (c) Mean response length during training.

Appendix figures & tables36 assets

Supplementary material from the paper’s appendix.

Appendix

Benchmark	Targets	Avg. reactions	Avg. depth
USPTO-190	190	6.7	6.0
ChEMBL-1000	1,000	–	–
Pistachio Reachable	150	5.5	5.4
Pistachio Hard	100	7.5	7.2

Appendix

Table 4: Summary of the multi-step retrosynthesis benchmarks. Reaction counts and depths refer to the provided reference routes; these statistics are unavailable for ChEMBL-1000.

Diagnostic	Value
Evaluation targets	1,440
Exact target overlap	0
Median maximum Morgan similarity	0.466
Targets with maximum similarity $\geq 0.8$	5.28%
Targets with novel Murcko scaffolds	81.49%
Success on novel-scaffold targets	60.26%

Appendix

Table 5: Structural separation between training and evaluation targets. Similarities are computed against the union of retained targets from all training stages. Success uses the same fixed single-trajectory evaluation as Table 1 .

Method	Params.	Pistachio Hard	Pistachio Reachable	USPTO-190	ChEMBL- 1000	Average (%)
Frontier LLMs (proprietary)
Claude Opus 4.8	—	38.57	77.50	54.44	55.63	56.54
Gemini 3.5 Flash	—	42.86	72.50	52.22	65.63	58.30
Grok 4.5	—	52.86	71.25	50.00	62.50	59.15
GPT-5.5	—	58.57	82.50	54.44	58.75	63.57
Frontier LLMs (open-weight)

Appendix

Table 6: LLM performance on the shared 400-target subset. Every model is evaluated on the same frozen targets using the success criterion in Eq. 10 . We report benchmark-specific success rates and their unweighted macro average. Em dashes denote undisclosed parameter counts. Gray shading marks the best result among frontier LLMs, and boldface marks the best result among mid-scale LLMs.

Setting	Value
Annotation model	Intern-S1
Source target–route pairs	104,867
Generations per route	1
Temperature	0.7
Top- $p$ / top- $k$	1.0 / 50
Reasoning mode	Enabled

Appendix

Table 7: Configuration used to generate retrosynthetic warm-up annotations with Intern-S1.

Setting	Value
Teacher model	DeepSeek-V4-Pro
Target source	USPTO-full
Rollout targets	2,200
Rollouts per target	8
Maximum inventory-query turns	3
Cumulative generation limit	32k tokens

Appendix

Table 8: Configuration used to generate and curate the teacher-guided cold-start trajectories.

Setting	Value
Generating policy	$\pi_{\mathrm{seed}}$
Target source	USPTO-full
Trajectory sampling	Best-of- $N$
Temperature / top- $p$	1.0 / 1.0
Maximum reasoning-and-action turns	5
Cumulative generation limit	32k tokens

Appendix

Table 9: Generation and curation configuration for reasoning bootstrapping.

Setting	Value
Initialization	$R^{3}$ retrosynthesis-pretrained Qwen3-8B checkpoint
Fine-tuning method	Full-parameter SFT
Examples / validation split	102,212 / 1,000
Training epochs	2
Maximum sequence length	4,096 tokens
Global batch size	64

Appendix

Table 10: Training configuration for the retrosynthetic-intuition warm-up.

Setting	Value
Initialization	$\pi_{\mathrm{warmed}}$
Fine-tuning method	Full-parameter SFT
Examples / validation split	700 / 50
Training epochs	2
Maximum sequence length	32,768 tokens
Global batch size	8

Appendix

Table 11: Training configuration for the teacher-guided cold start.

Setting	Value
Initialization	Teacher-guided cold-start policy
Optimization algorithm	GSPO
Training targets	10,000
Optimization updates	50
Rollout targets per batch	128
Rollouts per target	8

Appendix

Table 12: Training configuration for first-round RL.

Setting	Value
Initialization	$\pi_{\mathrm{warmed}}$
Fine-tuning method	Full-parameter SFT
Examples / validation split	9,550 / 100
Training epochs	1
Maximum sequence length	32,768 tokens
Global batch size	8

Appendix

Table 13: Training configuration for reasoning bootstrapping.

Setting	Value
Initialization	$\pi_{\mathrm{boot}}$
Optimization algorithm	GSPO
Training targets	10,000
Optimization updates	780
Rollout targets per batch	64
Rollouts per target	8

Appendix

Table 14: Training configuration for final-stage RL.

Figure 5: Retrosynthetic-intuition warm-up improves the optimization of subsequent reasoning-bootstrapping SFT. Without and with intuition warm-up, training starts from the retrosynthesis-pretrained backbone and

\pi_{\mathrm{warmed}}

, respectively. (a) Per-step training loss under the two matched initializations; pale traces show individual updates and solid curves show centered 61-update averages. (b) Validation loss throughout training.

SFT epochs	Without warm-up	With warm-up	$\Delta$	Without warm-up	With warm-up	$\Delta$
	Pass@1 (%)			Pass@8 (%)
1	24.99	26.42	+1.43	65.24	69.62	+4.38
2	30.21	32.61	+2.40	70.92	73.10	+2.18
3	28.50	32.94	+4.44	74.70	75.26	+0.56

Appendix

Table 15: Retrosynthetic-intuition warm-up consistently improves success rates under matched reasoning-bootstrapping SFT. Results are macro averages across the four benchmarks.

Epoch	Eval loss	SFT entropy	Macro P@1 (%)	Macro P@8 (%)	Rollout NLL	Final parse (%)
1	0.2850	0.3217	26.42	69.62	0.3151	71.53
2	0.2851	0.2830	32.61	73.10	0.2488	81.32
3	0.3058	0.2586	32.94	75.26	0.1927	77.01

Appendix

Table 16: Validation loss, entropy, and task performance after different numbers of reasoning-bootstrapping epochs. Macro success is averaged across the four benchmarks. Bold marks the column maximum, except for loss, where lower is better. Final parse denotes the rate of parseable final answers.

Figure 6: One epoch of reasoning bootstrapping yields stronger downstream RL. (a) Validation loss and held-out entropy throughout three epochs of SFT. (b) Validation success during matched final-stage RL initialized from the one- and three-epoch policies. RL validation uses the default rollout decoding configuration (

T=1.0

, top-

p=1.0

; different from the benchmark decoding configuration).

Figure 7: Final-stage RL yields large capability gains only after reasoning bootstrapping. (a) Validation success. (b) Mean reward over training rollouts. (c) Mean training-rollout response length. Pale traces in (b) and (c) show individual updates, and solid curves show centered 11-update averages. Both runs use the default rollout decoding configuration (

T=1.0

, top-

p=1.0

); curves are shown through update 600.

Figure 8: Effect of dynamic sampling during final-stage RL. (a) Validation success with and without dynamic sampling. (b) Mean reward over the training rollouts retained for each policy update. Pale traces show individual updates, and solid curves show centered 21-update averages. Validation uses the default rollout decoding configuration (

T=1.0

, top-

p=1.0

Feedback	Success (%)	Stock-complete (%)	Chemical validity (%)	Mean response tokens	Mean inventory queries
True	68.20	80.40	73.60	7,020	2.40
Shuffled	39.00	43.80	46.60	9,158	3.27
Masked	17.60	19.20	47.20	7,891	3.63

Appendix

Table 17: Paired intervention on inventory feedback. Results are computed from one trajectory for each of 500 held-out targets. The success rate requires a structurally valid, stock-complete route that passes the chemical-validity assessment.

	Performance			Inference behavior
Turn limit	Success rate (%)	Stock-complete routes (%)	Chemically valid routes (%)	Mean tool calls (all)	Mean tool calls (successful)	Mean response tokens	Truncated (%)
2	27.20	28.40	27.80	1.71	1.00	4,524	0.00
3	50.20	56.00	51.60	2.07	1.38	5,743	0.20
4	62.80	70.40	65.40	2.33	1.71	6,563	0.00
5	68.00	77.40	71.80	2.48	1.92	7,063	0.20
6	69.20	83.60	74.00	2.49	2.10	7,294	0.20

Appendix

Table 18: Held-out performance and inference behavior across turn limits. Blue shading marks the selected configuration, and bold marks the maximum in each performance column. Tool calls are averaged over all trajectories or successful trajectories only.

	Performance (%)			Generation behavior
$T$	Success rate	Stock- complete	Chemical validity	Token NLL	Response tokens	Inventory queries	Truncated (%)
0.6	65.80	76.00	69.60	0.1084	7,055	2.52	0.80
0.7	66.80	77.20	71.40	0.1322	6,911	2.48	0.40
0.8	68.00	77.40	71.80	0.1638	7,063	2.48	0.20
0.9	65.00	76.80	70.80	0.2103	7,595	2.47	0.60
1.0	63.00	73.60	70.80	0.2847	7,958	2.43	0.20

Appendix

Table 19: Inference performance and generation behavior across sampling temperatures. Results are computed on the frozen 500-target validation split. Blue shading marks the selected configuration; bold marks the highest task metrics and the lowest truncation rate.

Method	Params.	Success (%)	Output tokens	Inventory queries
Frontier LLMs (proprietary)
Claude Opus 4.8 †	—	56.54	3.57k	2.73
Gemini 3.5 Flash †	—	58.30	20.33k	2.70
Grok 4.5 †	—	59.15	14.30k	1.72
GPT-5.5 †	—	63.57	5.32k	2.57
Frontier LLMs (open-weight)

Appendix

Table 20: Inference cost of the evaluated LLMs. Success and usage are macro-averaged; daggers mark the shared 400-target subset, and em dashes denote undisclosed parameter counts.

			Synthetic accessibility		ChemCensor		Round trip
Method	Params.	Stock routes (/400)	Target–leaf $\Delta$ SA	Reducing steps (%)	RC routes /100 steps	FG routes /100 steps	Route RT@20 (%)
Frontier LLMs (proprietary)
Claude Opus 4.8	—	304	0.611	70.10	8.35	3.49	14.42
Gemini 3.5 Flash	—	300	0.607	68.60	9.89	6.02	15.75
Grok 4.5	—	295	0.618	63.89	8.48	4.48	13.06
GPT-5.5	—	289	0.539	66.57	9.98	5.33	10.90

Appendix

Table 21: Complementary in silico assessments on the shared 400-target subset. Stock routes are stock-complete.

\Delta

SA is the decrease from target SA to mean leaf SA; reducing steps have a lower mean precursor SA than product SA. ChemCensor columns report all-step-supported stock-complete routes per 100 evaluated reaction steps, macro-averaged across benchmarks. Route RT is the percentage of all 400 targets whose routes pass exact top-20 forward recovery at every step.

Orthogonal diagnostic	Primary pass (%)	Primary non-pass (%)	Difference (95% CI)
ChemCensor RC step support	59.72	45.92	$+13.80$ ( $6.65$ , $21.15$ )
ChemCensor FG step support	45.14	29.90	$+15.24$ ( $7.67$ , $22.76$ )
Exact route RT@20	14.98	0.00	$+14.98$ ( $10.67$ , $19.50$ )

Appendix

Table 22: Orthogonal assessments grouped by primary chemical validity. We split 293 stock-complete Solvent routes into routes that pass and routes that do not pass the primary chemical-validity assessment. Each row reports the mean score of an independent diagnostic in the two groups. Difference is the mean for primary-pass routes minus the mean for primary-non-pass routes; 95% confidence intervals use 10,000 benchmark-stratified bootstrap resamples.

Figure 9: Round 1 RL stabilizes long-horizon interaction. (a) Training reward, (b) mean response length, and (c) the fraction of truncated trajectories. Faint lines show per-update measurements and colored lines show centered seven-update moving averages. The dashed line marks the selected

\pi_{\mathrm{seed}}

; the shaded region denotes the exploratory continuation beyond this checkpoint.

Figure 10: Reward and entropy during final-stage RL. (a) Mean rollout reward and (b) policy entropy. Faint lines show per-update measurements and colored lines show centered 25-update moving averages.

Figure 11: Target molecule for the convergent-route case study.

Query	Proposed route boundary	Available leaves
1	Aniline and aryl isocyanate	0/2
2	Two anilines and CDI	1/3
3	Both aniline branches expanded to simpler precursors	6/6

Appendix

Table 23: Inventory feedback during route revision.

Figure 12: Inventory-verified convergent route rendered in forward synthetic order.

Figure 13: Target molecule for the Boc-protection case study.

Figure 14: Inventory-verified protecting-group route rendered in forward synthetic order.

Figure 15: Target molecule shared across independently sampled trajectories.

Figure 16: Stock-complete route based on benzyl substitution, rendered in forward synthetic order.

Figure 17: Stock-complete route centered on reductive amination, rendered in forward synthetic order.

Policy	Truncated (%)	Parseable final answer (%)	Malformed or incomplete interaction (%)
Teacher-guided SFT	78.27	12.93	71.32
$\pi_{\mathrm{seed}}$	0.79	70.91	39.69

Appendix

Table 24: Rollout reliability before and after Round 1 RL under matched decoding. Malformed or incomplete interactions fail the tool-trace validity check.

Policy	Tokens	Steps	Stock- complete	Chemical validity
Round 2 bootstrapping	6,449	4	Yes	No
Final-stage RL	3,935	3	Yes	Yes

Appendix

Table 25: Protection-group strategy before and after final-stage RL.

Figure 18: Routes proposed before and after final-stage RL, with steps shown in forward synthetic order. (a) The Round 2 bootstrapping route is stock-complete but fails the chemical-validity check because its initial methylation does not account for the exocyclic nitrogen. (b) The final-stage RL route carries Boc protection through the selective bond formation and removes it only in the final step.

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Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions. The vast combinatorial search space makes this task challenging even for expert chemists. Traditional methods combine tree search with offline-trained value networks that score candidates in isolation, without reasoning about complete multi-step routes. Recent work leverages Large Language Models (LLMs) for this task, but relies on simple interfaces that limit exploration of the full search space. We introduce RetroAgent, an LLM agent that bridges symbolic search and neural reasoning through a harness with structured memory. Through memory and chemistry tools, the agent observes the full search state, including explored routes, available alternatives, and properties of intermediates, enabling informed decisions grounded in both global progress and domain knowledge. Experiments on in-distribution and out-of-distribution benchmarks demonstrate that RetroAgent delivers strong performance and generalization.

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