Model R&D, training & reinforcement learning

Work within authorized data, model goals and compute budgets: pretraining assessment, SFT, preference optimization, RL and private deployment.

Discuss this service Project resource library

Model R&D, training & reinforcement learning — Original AI-generated concept scene; not a real customer, device or project result.

What we can develop

Model and data R&D: architectures, governance, rights and training sets.
Continued pretraining, SFT, LoRA/QLoRA and domain adaptation.
Preference optimization and RL: task-appropriate DPO, reward modeling and GRPO/PPO.
Inference optimization, evaluation, private serving, versioning and safety tests.
Task adaptation and evaluation for text, code, languages and multimodal models.
Annotation, quality filtering, synthetic-data validation and dataset governance.
Distributed training, checkpoints, inference optimization and serving.
Preference data, rewards, safety alignment and controlled-agent training.

Project resource library

Model R&D and training brief

Compare prompting, RAG, fine-tuning and continued pretraining before training from scratch. Specify parameters, context, tasks and licenses; fitting weights in memory does not establish affordable training.

Route selection
Data and budget
Training deliverables

Read the full brief

RL and model evaluation brief

SFT learns demonstrations; DPO optimizes preference pairs rather than online RL. GRPO/PPO need rewards, sampling and policy updates. Select methods by task and budget; RL is not mandatory.

Method boundaries
Rewards and risks
Independent acceptance

Read the full brief

Engineering data and integration brief

Inventory licenses, data, annotation, preferences, rewards and held-out tests. Count samples/tokens, lengths, languages, duplicates and privacy; check overlap. Prioritize rights and quality over scale and verify synthetic bias/contamination.

Data and collection definitions
Interfaces and integration
Operations and handover

Read the full brief

Scenario and solution studies

Concept studies illustrate design and acceptance, not completed customer projects.

Domain support-model adaptation

Project context: A base model lacks product terminology and reply format.
Solution approach: Test RAG first, then SFT/LoRA with authorized examples; retain knowledge updates in retrieval.
Acceptance focus: Compare held-out quality, abstention and costs; prevent leakage.

Policy training for verifiable tasks

Project context: A team needs reliable constrained-tool execution.
Solution approach: Define sandbox rewards and safety; compare SFT and GRPO without real funds or production equipment.
Acceptance focus: Check success, violations, reward hacking and cross-task generalization.

Multilingual domain evaluation

Project context: Business quality may vary by language.
Solution approach: Build authorized held-out tasks, compare base/RAG/tuning and report per language.
Acceptance focus: Check coverage, annotation agreement, leakage and failures, not one total score.

Reward research for controlled tool agents

Project context: Tool selection and task completion need verifiable rewards.
Solution approach: Sandbox success, violation and resource rewards; check gaming before small-scale training.
Acceptance focus: Evaluate held-out success, resource cost, boundary violations and reward robustness.

From requirements to handover

Requirements and authorization: define the problem, owners, data and interface rights.
Plan and baseline: agree scope, risks, budget, deliverables and acceptance.
Prototype and pilot: validate critical flows and recovery in controlled environments.
Integration and acceptance: review test evidence, not demonstrations alone.
Handover and maintenance: deliver docs, training, access, backups and iteration plans.

Data and acceptance measures

Measures guide project testing. Except for cited industry definitions, they are not achieved Xinan results or performance promises.

Training budget: disclose GPU-hours, tokens, sequence length, precision and parallelism.
Quality: evaluate held-out task categories; training loss is not business accuracy.
Public research case: SmolLM3-3B reports about 11.2T pretraining tokens; not Xinan capability.
Weight estimate: parameters × bytes; 3B BF16 weights are about 6GB decimal, excluding optimizer, activations and runtime.
Record samples, tokens, length distribution, duplicates and licenses; samples do not replace token budgets.
Throughput in tokens/s must disclose hardware, precision, parallelism, context and batch; training and inference are not interchangeable.

Sources and industry references

Third-party material is technical reference, not a partnership or endorsement. Checked: 2026-10-05.

Hugging Face TRL

Public SFT, DPO, GRPO and reward-model implementations; DPO is preference optimization, not online RL.

Hugging Face SmolLM3 training report

Public case: staged training of a 3B model illustrates data and compute planning; not Xinan work.

Before we start, tell us

Task, base-model license, data sources, samples, GPU budget, serving limits and safety.

Discuss this service sobosscn@gmail.com

Delivery and usage boundaries

Training resources depend on configuration; personal-box inference capability is not training capacity. No claim of general superintelligence or fixed gains.

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What we can develop

Model and data R&D: architectures, governance, rights and training sets.

Continued pretraining, SFT, LoRA/QLoRA and domain adaptation.

Preference optimization and RL: task-appropriate DPO, reward modeling and GRPO/PPO.

Inference optimization, evaluation, private serving, versioning and safety tests.

Task adaptation and evaluation for text, code, languages and multimodal models.

Annotation, quality filtering, synthetic-data validation and dataset governance.

Distributed training, checkpoints, inference optimization and serving.

Preference data, rewards, safety alignment and controlled-agent training.

Project resource library

Model R&D and training brief

Route selection
Data and budget
Training deliverables

Read the full brief

RL and model evaluation brief

SFT learns demonstrations; DPO optimizes preference pairs rather than online RL. GRPO/PPO need rewards, sampling and policy updates. Select methods by task and budget; RL is not mandatory.

Method boundaries
Rewards and risks
Independent acceptance

Read the full brief

Engineering data and integration brief

Data and collection definitions
Interfaces and integration
Operations and handover

Read the full brief

Scenario and solution studies

Concept studies illustrate design and acceptance, not completed customer projects.

Domain support-model adaptation

Project context: A base model lacks product terminology and reply format.
Solution approach: Test RAG first, then SFT/LoRA with authorized examples; retain knowledge updates in retrieval.
Acceptance focus: Compare held-out quality, abstention and costs; prevent leakage.

Policy training for verifiable tasks

Project context: A team needs reliable constrained-tool execution.
Solution approach: Define sandbox rewards and safety; compare SFT and GRPO without real funds or production equipment.
Acceptance focus: Check success, violations, reward hacking and cross-task generalization.

Multilingual domain evaluation

Project context: Business quality may vary by language.
Solution approach: Build authorized held-out tasks, compare base/RAG/tuning and report per language.
Acceptance focus: Check coverage, annotation agreement, leakage and failures, not one total score.

Reward research for controlled tool agents

Project context: Tool selection and task completion need verifiable rewards.
Solution approach: Sandbox success, violation and resource rewards; check gaming before small-scale training.
Acceptance focus: Evaluate held-out success, resource cost, boundary violations and reward robustness.

From requirements to handover

Requirements and authorization: define the problem, owners, data and interface rights.

Plan and baseline: agree scope, risks, budget, deliverables and acceptance.

Prototype and pilot: validate critical flows and recovery in controlled environments.

Integration and acceptance: review test evidence, not demonstrations alone.

Handover and maintenance: deliver docs, training, access, backups and iteration plans.

Data and acceptance measures

Measures guide project testing. Except for cited industry definitions, they are not achieved Xinan results or performance promises.

Training budget: disclose GPU-hours, tokens, sequence length, precision and parallelism.

Quality: evaluate held-out task categories; training loss is not business accuracy.

Public research case: SmolLM3-3B reports about 11.2T pretraining tokens; not Xinan capability.

Weight estimate: parameters × bytes; 3B BF16 weights are about 6GB decimal, excluding optimizer, activations and runtime.

Record samples, tokens, length distribution, duplicates and licenses; samples do not replace token budgets.

Throughput in tokens/s must disclose hardware, precision, parallelism, context and batch; training and inference are not interchangeable.